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1,294 | 3 | Title: Representation Requirements for Supporting Decision Model Formulation
Abstract: This paper outlines a methodology for analyzing the representational support for knowledge-based decision-modeling in a broad domain. A relevant set of inference patterns and knowledge types are identified. By comparing the analysis results to existing representations, some insights are gained into a design approach for integrating categorical and uncertain knowledge in a context sensitive manner. | [
915
] | Train |
1,295 | 2 | Title: On the Computational Utility of Consciousness
Abstract: We propose a computational framework for understanding and modeling human consciousness. This framework integrates many existing theoretical perspectives, yet is sufficiently concrete to allow simulation experiments. We do not attempt to explain qualia (subjective experience), but instead ask what differences exist within the cognitive information processing system when a person is conscious of mentally-represented information versus when that information is unconscious. The central idea we explore is that the contents of consciousness correspond to temporally persistent states in a network of computational modules. Three simulations are described illustrating that the behavior of persistent states in the models corresponds roughly to the behavior of conscious states people experience when performing similar tasks. Our simulations show that periodic settling to persistent (i.e., conscious) states improves performance by cleaning up inaccuracies and noise, forcing decisions, and helping keep the system on track toward a solution. | [
886
] | Train |
1,296 | 6 | Title: Sifting informative examples from a random source.
Abstract: We discuss two types of algorithms for selecting relevant examples that have been developed in the context of computation learning theory. The examples are selected out of a stream of examples that are generated independently at random. The first two algorithms are the so-called "boosting" algorithms of Schapire [ Schapire, 1990 ] and Fre-und [ Freund, 1990 ] , and the Query-by-Committee algorithm of Seung [ Seung et al., 1992 ] . We describe the algorithms and some of their proven properties, point to some of their commonalities, and suggest some possible future implications. | [
109,
456,
1198
] | Validation |
1,297 | 5 | Title: The Origins of Inductive Logic Programming: A Prehistoric Tale
Abstract: This paper traces the development of the main ideas that have led to the present state of knowledge in Inductive Logic Programming. The story begins with research in psychology on the subject of human concept learning. Results from this research influenced early efforts in Artificial Intelligence which combined with the formal methods of inductive inference to evolve into the present discipline of Inductive Logic Programming. Inductive Logic Programming is often considered to be a young discipline. However, it has its roots in research dating back nearly 40 years. This paper traces the development of ideas beginning in psychology and the effect they had on concept learning research in Artificial Intelligence. Independent of any requirement for a psychological basis, formal methods of inductive inference were developed. These separate streams eventually gave rise to Inductive Logic Programming. This account is not entirely unbiased. More attention is given to the work of those researchers who most influenced my own interest in machine learning. Being a retrospective paper, I do not attempt to describe recent developments in ILP. This account only includes research prior to 1991 the year in which the term Inductive Logic Programming was first used (Muggleton, 1991). This is the reason for the subtitle A Prehistoric Tale. The major headings in the paper are taken from the names of periods in the evolution of life on Earth. | [
1174
] | Test |
1,298 | 2 | Title: Rule Revision with Recurrent Neural Networks
Abstract: Recurrent neural networks readily process, recognize and generate temporal sequences. By encoding grammatical strings as temporal sequences, recurrent neural networks can be trained to behave like deterministic sequential finite-state automata. Algorithms have been developed for extracting grammatical rules from trained networks. Using a simple method for inserting prior knowledge (or rules) into recurrent neural networks, we show that recurrent neural networks are able to perform rule revision. Rule revision is performed by comparing the inserted rules with the rules in the finite-state automata extracted from trained networks. The results from training a recurrent neural network to recognize a known non-trivial, randomly generated regular grammar show that not only do the networks preserve correct rules but that they are able to correct through training inserted rules which were initially incorrect. (By incorrect, we mean that the rules were not the ones in the randomly generated grammar.) | [
407,
409,
1285,
1592
] | Test |
1,299 | 1 | Title: Multi-parent Recombination
Abstract: In this section we survey recombination operators that can apply more than two parents to create offspring. Some multi-parent recombination operators are defined for a fixed number of parents, e.g. have arity three, in some operators the number of parents is a random number that might be greater than two, and in yet other operators the arity is a parameter that can be set to an arbitrary integer number. We pay special attention to this latter type of operators and summarize results on the effect of operator arity on EA performance. | [
714,
1216,
1218,
1392
] | Train |
1,300 | 2 | Title: Tempering Backpropagation Networks: Not All Weights are Created Equal approach yields hitherto unparalleled performance on
Abstract: Backpropagation learning algorithms typically collapse the network's structure into a single vector of weight parameters to be optimized. We suggest that their performance may be improved by utilizing the structural information instead of discarding it, and introduce a framework for tempering each weight accordingly. In the tempering model, activation and error signals are treated as approximately independent random variables. The characteristic scale of weight changes is then matched to that of the residuals, allowing structural properties such as a node's fan-in and fan-out to affect the local learning rate and backpropagated error. The model also permits calculation of an upper bound on the global learning rate for batch updates, which in turn leads to different update rules for bias vs. non-bias weights. | [
1320,
1342
] | Train |
1,301 | 5 | Title: Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules
Abstract: This paper addresses a class of learning problems that require a construction of descriptions that combine both M-of-N rules and traditional Disjunctive Normal form (DNF) rules. The presented method learns such descriptions, which we call conditional M-of-N rules, using the hypothesis-driven constructive induction approach. In this approach, the representation space is modified according to patterns discovered in the iteratively generated hypotheses. The need for the M-of-N rules is detected by observing "exclusive-or" or "equivalence" patterns in the hypotheses. These patterns indicate symmetry relations among pairs of attributes. Symmetrical attributes are combined into maximal symmetry classes. For each symmetry class, the method constructs a "counting attribute" that adds a new dimension to the representation space. The search for hypothesis in iteratively modified representation spaces is done by the standard AQ inductive rule learning algorithm. It is shown that the proposed method is capable of solving problems that would be very difficult to tackle by any of the traditional symbolic learning methods. | [
960,
1266,
1595,
2346
] | Train |
1,302 | 0 | Title: Correcting for Length Biasing in Conversational Case Scoring
Abstract: Inference's conversational case-based reasoning (CCBR) approach, embedded in the CBR Content Navigator line of products, is susceptible to a bias in its case scoring algorithm. In particular, shorter cases tend to be given higher scores, assuming all other factors are held constant. This report summarizes our investigation for mediating this bias. We introduce an approach for eliminating this bias and evaluate how it affects retrieval performance for six case libraries. We also suggest explanations for these results, and note the limitations of our study. | [
983
] | Train |
1,303 | 1 | Title: Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms
Abstract: We investigate the effectiveness of stochastic hillclimbing as a baseline for evaluating the performance of genetic algorithms (GAs) as combinatorial function optimizers. In particular, we address four problems to which GAs have been applied in the literature: the maximum cut problem, Koza's 11-multiplexer problem, MDAP (the Multiprocessor Document Allocation Problem), and the jobshop problem. We demonstrate that simple stochastic hillclimbing methods are able to achieve results comparable or superior to those obtained by the GAs designed to address these four problems. We further illustrate, in the case of the jobshop problem, how insights obtained in the formulation of a stochastic hillclimbing algorithm can lead to improvements in the encoding used by a GA. fl Department of Computer Science, University of California at Berkeley. Supported by a NASA Graduate Fellowship. This paper was written while the author was a visiting researcher at the Ecole Normale Superieure-rue d'Ulm, Groupe de BioInformatique, France. E-mail: juels@cs.berkeley.edu y Department of Mathematics, University of California at Berkeley. Supported by an NDSEG Graduate Fellowship. E-mail: wattenbe@math.berkeley.edu | [
163,
343,
1274,
2202,
2347
] | Train |
1,304 | 0 | Title: Concept Sharing: A Means to Improve Multi-Concept Learning
Abstract: This paper describes several means for sharing between related concepts to improve learning in the same domain. The sharing comes in the form of substructures or possibly entire structures of previous concepts which may aid in learning other concepts. These substructures highlight useful information in the domain. Using two domains, we evaluate the effectiveness of concept sharing with respect to accuracy, concept size, search complexity, and noise resistance. | [
1354
] | Test |
1,305 | 1 | Title: Distribution Category: A Parallel Genetic Algorithm for the Set Partitioning Problem
Abstract: This work was supported by the Office of Scientific Computing, U.S. Department of Energy, under Contract W-31-109-Eng-38. It was submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate School of the Illinois Institute of Technology, May 1994 (thesis adviser: Dr. Tom Christopher). | [
163,
728,
1016,
1065,
1098,
1106,
1274,
1575,
1740
] | Test |
1,306 | 6 | Title: Improving the Accuracy and Speed of Support Vector Machines
Abstract: Support Vector Learning Machines (SVM) are finding application in pattern recognition, regression estimation, and operator inversion for ill-posed problems. Against this very general backdrop, any methods for improving the generalization performance, or for improving the speed in test phase, of SVMs are of increasing interest. In this paper we combine two such techniques on a pattern recognition problem. The method for improving generalization performance (the "virtual support vector" method) does so by incorporating known invariances of the problem. This method achieves a drop in the error rate on 10,000 NIST test digit images of 1.4% to 1.0%. The method for improving the speed (the "reduced set" method) does so by approximating the support vector decision surface. We apply this method to achieve a factor of fifty speedup in test phase over the virtual support vector machine. The combined approach yields a machine which is both 22 times faster than the original machine, and which has better generalization performance, achieving 1.1% error. The virtual support vector method is applicable to any SVM problem with known invariances. The reduced set method is applicable to any support vector machine. | [
607,
1050,
1310,
1499
] | Test |
1,307 | 2 | Title: Extracting Tree-Structured Representations of Trained Networks
Abstract: A significant limitation of neural networks is that the representations they learn are usually incomprehensible to humans. We present a novel algorithm, Trepan, for extracting comprehensible, symbolic representations from trained neural networks. Our algorithm uses queries to induce a decision tree that approximates the concept represented by a given network. Our experiments demonstrate that Trepan is able to produce decision trees that maintain a high level of fidelity to their respective networks while being comprehensible and accurate. Unlike previous work in this area, our algorithm is general in its applicability and scales well to large net works and problems with high-dimensional input spaces. | [
1057,
1657
] | Validation |
1,308 | 3 | Title: A Defect in Dempster-Shafer Theory
Abstract: By analyzing the relationships among chance, weight of evidence and degree of belief, it is shown that the assertion "chances are special cases of belief functions" and the assertion "Dempster's rule can be used to combine belief functions based on distinct bodies of evidence" together lead to an inconsistency in Dempster-Shafer theory. To solve this problem, some fundamental postulates of the theory must be rejected. A new approach for uncertainty management is introduced, which shares many intuitive ideas with D-S theory, while avoiding this problem. | [
1503,
1504,
1506,
1507
] | Test |
1,309 | 6 | Title: A Formalization of Explanation-Based Macro-operator Learning
Abstract: In spite of the popularity of Explanation-Based Learning (EBL), its theoretical basis is not well-understood. Using a generalization of Probably Approximately Correct (PAC) learning to problem solving domains, this paper formalizes two forms of Explanation-Based Learning of macro-operators and proves the sufficient conditions for their success. These two forms of EBL, called "Macro Caching" and "Serial Parsing," respectively exhibit two distinct sources of power or "bias": the sparseness of the solution space and the decomposability of the problem-space. The analysis shows that exponential speedup can be achieved when either of these biases is suitable for a domain. Somewhat surprisingly, it also shows that computing the preconditions of the macro-operators is not necessary to obtain these speedups. The theoretical results are confirmed by experiments in the domain of Eight Puzzle. Our work suggests that the best way to address the utility problem in EBL is to implement a bias which exploits the problem-space structure of the set of domains that one is interested in learning. | [
924,
1132,
1186
] | Validation |
1,310 | 6 | Title: Incorporating Invariances in Support Vector Learning Machines
Abstract: Developed only recently, support vector learning machines achieve high generalization ability by minimizing a bound on the expected test error; however, so far there existed no way of adding knowledge about invariances of a classification problem at hand. We present a method of incorporating prior knowledge about transformation invari-ances by applying transformations to support vectors, the training ex amples most critical for determining the classification boundary. | [
1306,
1499
] | Train |
1,311 | 1 | Title: ROBO-SHEPHERD: LEARNING COMPLEX ROBOTIC BEHAVIORS
Abstract: This paper reports on recent results using genetic algorithms to learn decision rules for complex robot behaviors. The method involves evaluating hypothetical rule sets on a simulator and applying simulated evolution to evolve more effective rules. The main contributions of this paper are (1) the task learned is a complex behavior involving multiple mobile robots, and (2) the learned rules are verified through experiments on operational mobile robots. The case study involves a shepherding task in which one mobile robot attempts to guide another robot to a specified area. | [
764,
910,
1140,
1253
] | Validation |
1,312 | 5 | Title: Learning Trees and Rules with Set-valued Features
Abstract: In most learning systems examples are represented as fixed-length "feature vectors", the components of which are either real numbers or nominal values. We propose an extension of the feature-vector representation that allows the value of a feature to be a set of strings; for instance, to represent a small white and black dog with the nominal features size and species and the set-valued feature color, one might use a feature vector with size=small, species=canis-familiaris and color=fwhite,blackg. Since we make no assumptions about the number of possible set elements, this extension of the traditional feature-vector representation is closely connected to Blum's "infinite attribute" representation. We argue that many decision tree and rule learning algorithms can be easily extended to set-valued features. We also show by example that many real-world learning problems can be efficiently and naturally represented with set-valued features; in particular, text categorization problems and problems that arise in propositionalizing first-order representations lend themselves to set-valued features. | [
344,
418,
638,
1260,
1269,
1428,
1622
] | Train |
1,313 | 2 | Title: TRAINREC: A System for Training Feedforward Simple Recurrent Networks Efficiently and Correctly
Abstract: In most learning systems examples are represented as fixed-length "feature vectors", the components of which are either real numbers or nominal values. We propose an extension of the feature-vector representation that allows the value of a feature to be a set of strings; for instance, to represent a small white and black dog with the nominal features size and species and the set-valued feature color, one might use a feature vector with size=small, species=canis-familiaris and color=fwhite,blackg. Since we make no assumptions about the number of possible set elements, this extension of the traditional feature-vector representation is closely connected to Blum's "infinite attribute" representation. We argue that many decision tree and rule learning algorithms can be easily extended to set-valued features. We also show by example that many real-world learning problems can be efficiently and naturally represented with set-valued features; in particular, text categorization problems and problems that arise in propositionalizing first-order representations lend themselves to set-valued features. | [
1005,
1382,
1623,
1655
] | Train |
1,314 | 4 | Title: Quick 'n' Dirty Generalization For Mobile Robot Learning Content Areas: robotics, reinforcement learning, machine learning,
Abstract: The mobile robot domain challenges policy-iteration reinforcement learning algorithms with difficult problems of structural credit assignment and uncertainty. Structural credit assignment is particularly acute in domains where "real-time" trial length is a limiting factor on the number of learning steps that physical hardware can perform. Noisy sensors and effectors in complex dynamic environments further complicate the learning problem, leading to situations where speed of learning and policy flexibility may be more important than policy optimality. Input generalization addresses these problems but is typically too time consuming for robot domains. We present two algorithms, YB-learning and YB , that perform simple and fast generalization of the input space based on bit-similarity. The algorithms trade off long-term optimality for immediate performance and flexibility. The algorithms were tested in simulation against non-generalized learning across different numbers of discounting steps, and YB was shown to perform better during the earlier stages of learning, particularly in the presence of noise. In trials performed on a sonar-based mobile robot subject to uncertainty of the "real world," YB surpassed the simulation results by a wide margin, strongly supporting the role of such "quick and dirty" generalization strategies in noisy real-time mobile robot domains. | [
1529
] | Test |
1,315 | 2 | Title: Modeling Volatility using State Space Models
Abstract: In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, empirical volatilities (the squared relative returns of prices) exhibit a significant amount of observational noise. To model and predict their time evolution adequately, we estimate state space models that explicitly include observational noise. We obtain relaxation times for shocks in the logarithm of volatility ranging from three weeks (for foreign exchange) to three to five months (for stock indices). In most cases, a two-dimensional hidden state is required to yield residuals that are consistent with white noise. We compare these results with ordinary autoregressive models (without a hidden state) and find that autoregressive models underestimate the relaxation times by about two orders of magnitude due to their ignoring the distinction between observational and dynamic noise. This new interpretation of the dynamics of volatility in terms of relaxators in a state space model carries over to stochastic volatility models and to GARCH models, and is useful for several problems in finance, including risk management and the pricing of derivative securities. | [
611,
668,
1079,
2452,
2595
] | Train |
1,316 | 4 | Title: KnightCap: A chess program that learns by combining TD() with minimax search
Abstract: In this paper we present TDLeaf(), a variation on the TD() algorithm that enables it to be used in conjunction with minimax search. We present some experiments in which our chess program, KnightCap, used TDLeaf() to learn its evaluation function while playing on the Free Ineternet Chess Server (FICS, fics.onenet.net). It improved from a 1650 rating to a 2100 rating in just 308 games and 3 days of play. We discuss some of the reasons for this success and also the relationship between our results and Tesauro's results in backgammon. | [
295,
565,
882
] | Test |
1,317 | 0 | Title: Use of Analogy in Automated Theorem Proving
Abstract: Technical Report ATP-90, Artificial Intelligence Laboratory, University of Texas at Austin. | [
1354
] | Train |
1,318 | 2 | Title: Misclassification Minimization
Abstract: The problem of minimizing the number of misclassified points by a plane, attempting to separate two point sets with intersecting convex hulls in n-dimensional real space, is formulated as a linear program with equilibrium constraints (LPEC). This general LPEC can be converted to an exact penalty problem with a quadratic objective and linear constraints. A Frank-Wolfe-type algorithm is proposed for the penalty problem that terminates at a stationary point or a global solution. Novel aspects of the approach include: (i) A linear complementarity formulation of the step function that "counts" misclassifications, (ii) Exact penalty formulation without boundedness, nondegeneracy or constraint qualification assumptions, (iii) An exact solution extraction from the sequence of minimizers of the penalty function for a finite value of the penalty parameter for the general LPEC and an explicitly exact solution for the LPEC with uncoupled constraints, and (iv) A parametric quadratic programming formulation of the LPEC associated with the misclassification minimization problem. | [
142,
227,
427,
1283
] | Validation |
1,319 | 1 | Title: The Ecology of Echo Echo is a generic ecosystem model in which evolving agents are
Abstract: The problem of minimizing the number of misclassified points by a plane, attempting to separate two point sets with intersecting convex hulls in n-dimensional real space, is formulated as a linear program with equilibrium constraints (LPEC). This general LPEC can be converted to an exact penalty problem with a quadratic objective and linear constraints. A Frank-Wolfe-type algorithm is proposed for the penalty problem that terminates at a stationary point or a global solution. Novel aspects of the approach include: (i) A linear complementarity formulation of the step function that "counts" misclassifications, (ii) Exact penalty formulation without boundedness, nondegeneracy or constraint qualification assumptions, (iii) An exact solution extraction from the sequence of minimizers of the penalty function for a finite value of the penalty parameter for the general LPEC and an explicitly exact solution for the LPEC with uncoupled constraints, and (iv) A parametric quadratic programming formulation of the LPEC associated with the misclassification minimization problem. | [
1391
] | Train |
1,320 | 2 | Title: On Centering Neural Network Weight Updates
Abstract: Technical Report IDSIA-19-97 Abstract. It has long been known that neural networks can learn faster when their input and hidden unit activities are centered about zero; recently we have extended this approach to also encompass the centering of error signals (Schraudolph and Sejnowski, 1996). Here we generalize this notion to all factors involved in the weight update, leading us to propose centering the slope of hidden unit activation functions as well. Slope centering removes the linear component of backpropagated error; this improves credit assignment in networks with shortcut connections. Benchmark results show that this can speed up learning significantly without adversely affecting the trained network's generalization ability. | [
359,
808,
1300
] | Validation |
1,321 | 2 | Title: Priority ASOCS ASOCS models have two significant advantages over other learning models:
Abstract: This paper presents an ASOCS (Adaptive Self-Organizing Concurrent System) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic control. An ASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. An ASOCS can operate in either a data processing mode or a learning mode. During data processing mode, an ASOCS acts as a parallel hardware circuit. During learning mode, an ASOCS incorporates a rule expressed as a Boolean conjunction in a distributed fashion in time logarithmic in the number of rules. This paper proposes a learning algorithm and architecture for Priority ASOCS. This new ASOCS model uses rules with priorities. The new model has significant learning time and space complexity improvements over previous models. Non-von Neumann architectures such as neural networks attack the word-at-a-time bottleneck of traditional computing systems [1]. Neural networks learn input-output mappings using highly distributed processing and memory [10,11,12]. Their numerous simple processing elements with modifiable weighted links permit a high degree of parallelism. A typical neural network has fixed topology. It learns by modifying weighted links between nodes. A new class of connectionist architectures has been proposed called ASOCS (Adaptive Self-Organizing Concurrent Systems) [4,5]. ASOCS models support efficient computation through self-organized learning and parallel execution. Learning is done through the incremental presentation of rules and/or examples. ASOCS models learn by modifying their topology. Data types include Boolean and multi-state variables; recent models support analog variables. The model incorporates rules into an adaptive logic network in a parallel and self organizing fashion. In processing mode, ASOCS supports fully parallel execution on actual inputs according to the learned rules. The adaptive logic network acts as a parallel hardware circuit during execution, mapping n input boolean vectors into m output boolean vectors, in a combinatoric fashion. The overall philosophy of ASOCS follows the high level goals of current neural network models. However, the mechanisms of learning and execution vary significantly. The ASOCS logic network is topologically dynamic with the network growing to efficiently fit the specific application. Current ASOCS models are based on digital nodes. ASOCS also supports use of symbolic and heuristic learning mechanisms, thus combining the parallelism and distributed nature of connectionist computing with the potential power of AI symbolic learning. A proof of concept ASOCS chip has been developed [2]. | [
297,
814,
1041,
1080,
1190,
1222
] | Validation |
1,322 | 6 | Title: Theory and Applications of Agnostic PAC-Learning with Small Decision Trees
Abstract: We exhibit a theoretically founded algorithm T2 for agnostic PAC-learning of decision trees of at most 2 levels, whose computation time is almost linear in the size of the training set. We evaluate the performance of this learning algorithm T2 on 15 common real-world datasets, and show that for most of these datasets T2 provides simple decision trees with little or no loss in predictive power (compared with C4.5). In fact, for datasets with continuous attributes its error rate tends to be lower than that of C4.5. To the best of our knowledge this is the first time that a PAC-learning algorithm is shown to be applicable to real-world classification problems. Since one can prove that T2 is an agnostic PAC-learning algorithm, T2 is guaranteed to produce close to optimal 2-level decision trees from sufficiently large training sets for any (!) distribution of data. In this regard T2 differs strongly from all other learning algorithms that are considered in applied machine learning, for which no guarantee can be given about their performance on new datasets. We also demonstrate that this algorithm T2 can be used as a diagnostic tool for the investigation of the expressive limits of 2-level decision trees. Finally, T2, in combination with new bounds on the VC-dimension of decision trees of bounded depth that we derive, provides us now for the first time with the tools necessary for comparing learning curves of decision trees for real-world datasets with the theoretical estimates of PAC learning theory. | [
323,
1020,
1027,
1622,
2539
] | Train |
1,323 | 2 | Title: On the Distribution of Performance from Multiple Neural Network Trials, On the Distribution of Performance
Abstract: Andrew D. Back was with the Department of Electrical and Computer Engineering, University of Queensland. St. Lucia, Australia. He is now with the Brain Information Processing Group, Frontier Research Program, RIKEN, The Institute of Physical and Chemical Research, 2-1 Hirosawa, Wako-shi, Saitama 351-01, Japan Abstract The performance of neural network simulations is often reported in terms of the mean and standard deviation of a number of simulations performed with different starting conditions. However, in many cases, the distribution of the individual results does not approximate a Gaussian distribution, may not be symmetric, and may be multimodal. We present the distribution of results for practical problems and show that assuming Gaussian distributions can significantly affect the interpretation of results, especially those of comparison studies. For a controlled task which we consider, we find that the distribution of performance is skewed towards better performance for smoother target functions and skewed towards worse performance | [
1062,
1145,
1149,
1150,
1195
] | Train |
1,324 | 3 | Title: [6] D. Geiger. Graphoids: a qualitative framework for probabilistic inference. An introduction to algorithms for
Abstract: Andrew D. Back was with the Department of Electrical and Computer Engineering, University of Queensland. St. Lucia, Australia. He is now with the Brain Information Processing Group, Frontier Research Program, RIKEN, The Institute of Physical and Chemical Research, 2-1 Hirosawa, Wako-shi, Saitama 351-01, Japan Abstract The performance of neural network simulations is often reported in terms of the mean and standard deviation of a number of simulations performed with different starting conditions. However, in many cases, the distribution of the individual results does not approximate a Gaussian distribution, may not be symmetric, and may be multimodal. We present the distribution of results for practical problems and show that assuming Gaussian distributions can significantly affect the interpretation of results, especially those of comparison studies. For a controlled task which we consider, we find that the distribution of performance is skewed towards better performance for smoother target functions and skewed towards worse performance | [
260,
1543,
1747,
2076
] | Validation |
1,325 | 1 | Title: Environmental Effects on Minimal Behaviors in the Minimat World
Abstract: The structure of an environment affects the behaviors of the organisms that have evolved in it. How is that structure to be described, and how can its behavioral consequences be explained and predicted? We aim to establish initial answers to these questions by simulating the evolution of very simple organisms in simple environments with different structures. Our artificial creatures, called "minimats," have neither sensors nor memory and behave solely by picking amongst the actions of moving, eating, reproducing, and sitting, according to an inherited probability distribution. Our simulated environments contain only food (and multiple minimats) and are structured in terms of their spatial and temporal food density and the patchiness with which the food appears. Changes in these environmental parameters affect the evolved behaviors of minimats in different ways, and all three parameters are of importance in describing the minimat world. One of the most useful behavioral strategies that evolves is "looping" movement, which allows minimats-despite their lack of internal state-to match their behavior to the temporal (and spatial) structure of their environment. Ultimately we find that minimats construct their own environments through their individual behaviors, making the study of the impact of global environment structure on individual behavior much more complex. | [
219,
1175,
2170,
2309
] | Train |
1,326 | 3 | Title: Causal diagrams for empirical research
Abstract: The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subject-matter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if the assumptions available are sufficient for identifying causal effects from nonexperimental data. If so the diagrams can be queried to produce mathematical expressions for causal effects in terms of observed distributions; otherwise, the diagrams can be queried to suggest additional observations or auxiliary experiments from which the desired inferences can be obtained. Key words: Causal inference, graph models, structural equations, treatment effect. | [
105,
248,
419,
776,
971,
1602,
1747,
2144,
2161,
2434
] | Train |
1,327 | 5 | Title: On Biases in Estimating Multi-Valued Attributes
Abstract: We analyse the biases of eleven measures for estimating the quality of the multi-valued attributes. The values of information gain, J-measure, gini-index, and relevance tend to linearly increase with the number of values of an attribute. The values of gain-ratio, distance measure, Relief , and the weight of evidence decrease for informative attributes and increase for irrelevant attributes. The bias of the statistic tests based on the chi-square distribution is similar but these functions are not able to discriminate among the attributes of different quality. We also introduce a new function based on the MDL principle whose value slightly decreases with the increasing number of attribute's values. | [
638,
1165,
1569
] | Train |
1,328 | 2 | Title: A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features
Abstract: In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of the feature space is required. We introduce a nearest neighbor algorithm for learning in domains with symbolic features. Our algorithm calculates distance tables that allow it to produce real-valued distances between instances, and attaches weights to the instances to further modify the structure of feature space. We show that this technique produces excellent classification accuracy on three problems that have been studied by machine learning researchers: predicting protein secondary structure, identifying DNA promoter sequences, and pronouncing English text. Direct experimental comparisons with the other learning algorithms show that our nearest neighbor algorithm is comparable or superior in all three domains. In addition, our algorithm has advantages in training speed, simplicity, and perspicuity. We conclude that experimental evidence favors the use and continued development of nearest neighbor algorithms for domains such as the ones studied here. | [
783,
785,
927,
947,
1020,
1031,
1101,
1107,
1109,
1111,
1155,
1173,
1412,
1423,
1513,
1568,
1584,
1644
] | Train |
1,329 | 6 | Title: Supervised and Unsupervised Discretization of Continuous Features
Abstract: Many supervised machine learning algorithms require a discrete feature space. In this paper, we review previous work on continuous feature discretization, identify defining characteristics of the methods, and conduct an empirical evaluation of several methods. We compare binning, an unsupervised discretization method, to entropy-based and purity-based methods, which are supervised algorithms. We found that the performance of the Naive-Bayes algorithm significantly improved when features were discretized using an entropy-based method. In fact, over the 16 tested datasets, the discretized version of Naive-Bayes slightly outperformed C4.5 on average. We also show that in some cases, the performance of the C4.5 induction algorithm significantly improved if features were discretized in advance; in our experiments, the performance never significantly degraded, an interesting phenomenon considering the fact that C4.5 is capable of locally discretiz ing features. | [
1020,
1049,
1071,
1986,
2127
] | Test |
1,330 | 1 | Title: Evolving Cellular Automata with Genetic Algorithms: A Review of Recent Work
Abstract: We review recent work done by our group on applying genetic algorithms (GAs) to the design of cellular automata (CAs) that can perform computations requiring global coordination. A GA was used to evolve CAs for two computational tasks: density classification and synchronization. In both cases, the GA discovered rules that gave rise to sophisticated emergent computational strategies. These strategies can be analyzed using a "computational mechanics" framework in which "particles" carry information and interactions between particles effects information processing. This framework can also be used to explain the process by which the strategies were designed by the GA. The work described here is a first step in employing GAs to engineer useful emergent computation in decentralized multi-processor systems. It is also a first step in understanding how an evolutionary process can produce complex systems with sophisticated collective computational abilities. | [
793,
1167
] | Train |
1,331 | 1 | Title: Mechanisms of Emergent Computation in Cellular Automata
Abstract: We introduce a class of embedded-particle models for describing the emergent computational strategies observed in cellular automata (CAs) that were evolved for performing certain computational tasks. The models are evaluated by comparing their estimated performances with the actual performances of the CAs they model. The results show, via a close quantitative agreement, that the embedded-particle framework captures the main information processing mechanisms of the emergent computation that arise in these evolved CAs. | [
1167
] | Test |
1,332 | 1 | Title: Statistical Dynamics of the Royal Road Genetic Algorithm
Abstract: Metastability is a common phenomenon. Many evolutionary processes, both natural and artificial, alternate between periods of stasis and brief periods of rapid change in their behavior. In this paper an analytical model for the dynamics of a mutation-only genetic algorithm (GA) is introduced that identifies a new and general mechanism causing metastability in evolutionary dynamics. The GA's population dynamics is described in terms of flows in the space of fitness distributions. The trajectories through fitness distribution space are derived in closed form in the limit of infinite populations. We then show how finite populations induce metastability, even in regions where fitness does not exhibit a local optimum. In particular, the model predicts the occurrence of "fitness epochs"| periods of stasis in population fitness distributions|at finite population size and identifies the locations of these fitness epochs with the flow's hyperbolic fixed points. This enables exact predictions of the metastable fitness distributions during the fitness epochs, as well as giving insight into the nature of the periods of stasis and the innovations between them. All these results are obtained as closed-form expressions in terms of the GA's parameters. An analysis of the Jacobian matrices in the neighborhood of an epoch's metastable fitness distribution allows for the calculation of its stable and unstable manifold dimensions and so reveals the state space's topological structure. More general quantitative features of the dynamics|fitness fluctuation amplitudes, epoch stability, and speed of the innovations|are also determined from the Jacobian eigenvalues. The analysis shows how quantitative predictions for a range of dynamical behaviors, that are specific to the finite population dynamics, can be derived from the solution of the infinite population dynamics. The theoretical predictions are shown to agree very well with statistics from GA simulations. We also discuss the connections of our results with those from population genetics and molecular evolution theory. | [
1167
] | Train |
1,333 | 1 | Title: Using Genetic Algorithms for Supervised Concept Learning
Abstract: Genetic Algorithms (GAs) have traditionally been used for non-symbolic learning tasks. In this chapter we consider the application of a GA to a symbolic learning task, supervised concept learning from examples. A GA concept learner (GABL) is implemented that learns a concept from a set of positive and negative examples. GABL is run in a batch-incremental mode to facilitate comparison with an incremental concept learner, ID5R. Preliminary results support that, despite minimal system bias, GABL is an effective concept learner and is quite competitive with ID5R as the target concept increases in complexity. | [
163,
578,
793,
1136,
1207,
1224,
1225,
1369,
1467,
1514,
1708
] | Test |
1,334 | 1 | Title: THE OPTIONS DESIGN EXPLORATION SYSTEM Reference Manual and User Guide Version B2.1
Abstract: Genetic Algorithms (GAs) have traditionally been used for non-symbolic learning tasks. In this chapter we consider the application of a GA to a symbolic learning task, supervised concept learning from examples. A GA concept learner (GABL) is implemented that learns a concept from a set of positive and negative examples. GABL is run in a batch-incremental mode to facilitate comparison with an incremental concept learner, ID5R. Preliminary results support that, despite minimal system bias, GABL is an effective concept learner and is quite competitive with ID5R as the target concept increases in complexity. | [
163,
793,
1130,
1696
] | Train |
1,335 | 3 | Title: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Abstract: We review accuracy estimation methods and compare the two most common methods: cross-validation and bootstrap. Recent experimental results on artificial data and theoretical results in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensive leave-one-out cross-validation. We report on a large-scale experiment|over half a million runs of C4.5 and a Naive-Bayes algorithm|to estimate the effects of different parameters on these algorithms on real-world datasets. For cross-validation, we vary the number of folds and whether the folds are stratified or not; for bootstrap, we vary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, the best method to use for model selection is ten-fold stratified cross validation, even if computation power allows using more folds. | [
885,
944,
1024,
1032,
1223,
1267,
1270,
1337,
1339,
1478,
1512,
1607
] | Validation |
1,336 | 3 | Title: Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid
Abstract: Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classification tasks even when the conditional independence assumption on which they are based is violated. However, most studies were done on small databases. We show that in some larger databases, the accuracy of Naive-Bayes does not scale up as well as decision trees. We then propose a new algorithm, NBTree, which induces a hybrid of decision-tree classifiers and Naive-Bayes classifiers: the decision-tree nodes contain uni-variate splits as regular decision-trees, but the leaves contain Naive-Bayesian classifiers. The approach retains the interpretability of Naive-Bayes and decision trees, while resulting in classifiers that frequently outperform both constituents, especially in the larger databases tested. | [
1027,
1478
] | Validation |
1,337 | 6 | Title: MLC A Machine Learning Library in C
Abstract: We present MLC ++ , a library of C ++ classes and tools for supervised Machine Learning. While MLC ++ provides general learning algorithms that can be used by end users, the main objective is to provide researchers and experts with a wide variety of tools that can accelerate algorithm development, increase software reliability, provide comparison tools, and display information visually. More than just a collection of existing algorithms, MLC ++ is an attempt to extract commonalities of algorithms and decompose them for a unified view that is simple, coherent, and extensible. In this paper we discuss the problems MLC ++ aims to solve, the design of MLC ++ , and the current functionality. | [
944,
1020,
1335,
2300,
2343
] | Test |
1,338 | 3 | Title: Computing Nonparametric Hierarchical Models
Abstract: Bayesian models involving Dirichlet process mixtures are at the heart of the modern nonparametric Bayesian movement. Much of the rapid development of these models in the last decade has been a direct result of advances in simulation-based computational methods. Some of the very early work in this area, circa 1988-1991, focused on the use of such nonparametric ideas and models in applications of otherwise standard hierarchical models. This chapter provides some historical review and perspective on these developments, with a prime focus on the use and integration of such nonparametric ideas in hierarchical models. We illustrate the ease with which the strict parametric assumptions common to most standard Bayesian hierarchical models can be relaxed to incorporate uncertainties about functional forms using Dirichlet process components, partly enabled by the approach to computation using MCMC methods. The resulting methology is illustrated with two examples taken from an unpublished 1992 report on the topic. | [
784,
855,
917,
1015,
1654
] | Train |
1,339 | 6 | Title: An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms
Abstract: In this paper we present an average-case analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noise-free Boolean attributes. We calculate the probability that the algorithm will induce an arbitrary pair of concept descriptions and then use this to compute the probability of correct classification over the instance space. The analysis takes into account the number of training instances, the number of attributes, the distribution of these attributes, and the level of class noise. We also explore the behavioral implications of the analysis by presenting predicted learning curves for artificial domains, and give experimental results on these domains as a check on our reasoning. One goal of research in machine learning is to discover principles that relate algorithms and domain characteristics to behavior. To this end, many researchers have carried out systematic experimentation with natural and artificial domains in search of empirical regularities (e.g., Kibler & Langley, 1988). Others have focused on theoretical analyses, often within the paradigm of probably approximately correct learning (e.g., Haus-sler, 1990). However, most experimental studies are based only on informal analyses of the learning task, whereas most formal analyses address the worst case, and thus bear little relation to empirical results. ber of attributes, and the class and attribute frequencies, they obtain predictions about the behavior of induction algorithms and used experiments to check their analyses. 1 However, their research does not focus on algorithms typically used by the experimental and practical sides of machine learning, and it is important that average-case analyses be extended to such methods. Recently, there has been growing interest in probabilistic approaches to inductive learning. For example, Fisher (1987) has described Cobweb, an incremental algorithm for conceptual clustering that draws heavily on Bayesian ideas, and the literature reports a number of systems that build on this work (e.g., Allen & Lang-ley, 1990; Iba & Gennari, 1991; Thompson & Langley, 1991). Cheeseman et al. (1988) have outlined Auto-Class, a nonincremental system that uses Bayesian methods to cluster instances into groups, and other researchers have focused on the induction of Bayesian inference networks (e.g., Cooper & Kerskovits, 1991). These recent Bayesian learning algorithms are complex and not easily amenable to analysis, but they share a common ancestor that is simpler and more tractable. This supervised algorithm, which we refer to simply as a Bayesian classifier, comes originally from work in pattern recognition (Duda & Hart, 1973). The method stores a probabilistic summary for each class; this summary contains the conditional probability of each attribute value given the class, as well as the probability (or base rate) of the class. This data structure approximates the representational power of a perceptron; it describes a single decision boundary through the instance space. When the algorithm encounters a new instance, it updates the probabilities stored with the specified class. Neither the order of training instances nor the occurrence of classification errors have any effect on this process. When given a test instance, the classifier uses an evaluation function (which we describe in detail later) to rank the alter | [
434,
1111,
1335,
1570,
1678,
2443,
2677
] | Train |
1,340 | 2 | Title: ADAPTIVE REGULARIZATION
Abstract: Regularization, e.g., in the form of weight decay, is important for training and optimization of neural network architectures. In this work we provide a tool based on asymptotic sampling theory, for iterative estimation of weight decay parameters. The basic idea is to do a gradient descent in the estimated generalization error with respect to the regularization parameters. The scheme is implemented in our Designer Net framework for network training and pruning, i.e., is based on the diagonal Hessian approximation. The scheme does not require essential computational overhead in addition to what is needed for training and pruning. The viability of the approach is demonstrated in an experiment concerning prediction of the chaotic Mackey-Glass series. We find that the optimized weight decays are relatively large for densely connected networks in the initial pruning phase, while they decrease as pruning proceeds. | [
157,
427,
1075
] | Validation |
1,341 | 2 | Title: Growing Layers of Perceptrons: Introducing the Extentron Algorithm
Abstract: vations of perceptrons: (1) when the perceptron learning algorithm cycles among hyperplanes, the hyperplanes may be compared to select one that gives a best split of the examples, and (2) it is always possible for the perceptron to build a hyper- plane that separates at least one example from all the rest. We describe the Extentron which grows multi-layer networks capable of distinguishing non- linearly-separable data using the simple perceptron rule for linear threshold units. The resulting algorithm is simple, very fast, scales well to large prob - lems, retains the convergence properties of the perceptron, and can be completely specified using only two parameters. Results are presented comparing the Extentron to other neural network paradigms and to symbolic learning systems. | [
812,
1044
] | Train |
1,342 | 2 | Title: Centering Neural Network Gradient Factors
Abstract: Technical Report IDSIA-19-97 Abstract. It has long been known that neural networks can learn faster when their input and hidden unit activities are centered about zero; recently we have extended this approach to also encompass the centering of error signals [2]. Here we generalize this notion to all factors involved in the network's gradient, leading us to propose centering the slope of hidden unit activation functions as well. Slope centering removes the linear component of backpropagated error; this improves credit assignment in networks with shortcut connections. Benchmark results show that this can speed up learning significantly without adversely affecting the trained network's generalization ability. | [
359,
808,
1300,
2454
] | Validation |
1,343 | 6 | Title: Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle
Abstract: We introduce a new fault-tolerant model of algorithmic learning using an equivalence oracle and an incomplete membership oracle, in which the answers to a random subset of the learner's membership queries may be missing. We demonstrate that, with high probability, it is still possible to learn monotone DNF formulas in polynomial time, provided that the fraction of missing answers is bounded by some constant less than one. Even when half the membership queries are expected to yield no information, our algorithm will exactly identify m-term, n-variable monotone DNF formulas with an expected O(mn 2 ) queries. The same task has been shown to require exponential time using equivalence queries alone. We extend the algorithm to handle some one-sided errors, and discuss several other possible error models. It is hoped that this work may lead to a better understanding of the power of membership queries and the effects of faulty teachers on query models of concept learning. | [
672,
1003,
1004,
1363,
1364,
1456
] | Train |
1,344 | 0 | Title: Discovery of Physical Principles from Design Experiences
Abstract: One method for making analogies is to access and instantiate abstract domain principles, and one method for acquiring knowledge of abstract principles is to discover them from experience. We view generalization over experiences in the absence of any prior knowledge of the target principle as the task of hypothesis formation, a subtask of discovery. Also, we view the use of the hypothesized principles for analogical design as the task of hypothesis testing, another subtask of discovery. In this paper, we focus on discovery of physical principles by generalization over design experiences in the domain of physical devices. Some important issues in generalization from experiences are what to generalize from an experience, how far to generalize, and what methods to use. We represent a reasoner's comprehension of specific designs in the form of structure-behavior-function (SBF) models. An SBF model provides a functional and causal explanation of the working of a device. We represent domain principles as device-independent behavior-function (BF) models. We show that (i) the function of a device determines what to generalize from its SBF model, (ii) the SBF model itself suggests how far to generalize, and (iii) the typology of functions indicates what method to use. | [
1046,
1047,
1121,
1138,
1345
] | Validation |
1,345 | 0 | Title: Use of Mental Models for Constraining Index Learning in Experience-Based Design
Abstract: The power of the case-based method comes from the ability to retrieve the "right" case when a new problem is specified. This implies that learning the "right" indices to a case before storing it for potential reuse is crucial for the success of the method. A hierarchical organization of the case memory raises two distinct but related issues in index learning: learning the indexing vocabulary, and learning the right level of generalization. In this paper we show how the use of structure-behavior-function (SBF) models constrains index learning in the context of experience-based design of physical devices. The SBF model of a design provides the functional and causal explanation of how the structure of the design delivers its function. We describe how the SBF model of a design, together with a specification of the task for which the design case might be reused, provides the vocabulary for indexing the design case in memory. We also discuss how the prior design experiences stored in case-memory help to determine the level of index generalization. The KRITIK2 system implements and evaluates the model-based method for learning indices to design cases. | [
1046,
1047,
1344,
1640
] | Test |
1,346 | 2 | Title: References Linear Controller Design, Limits of Performance, "The parallel projection operators of a nonlinear feedback
Abstract: 13] Yang, Y., H.J. Sussmann, and E.D. Sontag, "Stabilization of linear systems with bounded controls," in Proc. Nonlinear Control Systems Design Symp., Bordeaux, June 1992 (M. Fliess, Ed.), IFAC Publications, pp. 15-20. Journal version to appear in IEEE Trans. Autom. Control . | [
1272,
1281,
1451
] | Test |
1,347 | 3 | Title: Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-linear Models
Abstract: The Bayesian approach to comparing models involves calculating the posterior probability of each plausible model. For high-dimensional contingency tables, the set of plausible models is very large. We focus attention on reversible jump Markov chain Monte Carlo (Green, 1995) and develop strategies for calculating posterior probabilities of hierarchical, graphical or decomposable log-linear models. Even for tables of moderate size, these sets of models may be very large. The choice of suitable prior distributions for model parameters is also discussed in detail, and two examples are presented. For the first example, a 2 fi 3 fi 4 table, the model probabilities calculated using our reversible jump approach are compared with model probabilities calculated exactly or by using an alternative approximation. The second example is a 2 6 contingency table for which exact methods are infeasible, due to the large number of possible models. | [
84,
1147,
1240,
1241
] | Train |
1,348 | 0 | Title: Learning Indices for Schema Selection
Abstract: In addition to learning new knowledge, a system must be able to learn when the knowledge is likely to be applicable. An index is a piece of information which, when identified in a given situation, triggers the relevant piece of knowledge (or schema) in the system's memory. We discuss the issue of how indices may be learned automatically in the context of a story understanding task, and present a program that can learn new indices for existing explanatory schemas. We discuss two methods using which the system can identify the relevant schema even if the input does not directly match an existing index, and learn a new index to allow it to retrieve this schema more efficiently in the future. | [
612,
1047,
1535,
1537
] | Test |
1,349 | 4 | Title: Robust performance and adaptation using receding horizon H 1 control of time varying systems.
Abstract: In this paper we construct suboptimal H 1 controllers which satisfy a new robust performance condition, using the receding horizon technique. A method is described for the synthesis of H 1 controllers online, making use of the exact plant model only on a finite interval extending into the future. Inequalities based on the two Riccati differential equation solution to the finite horizon H 1 problem are derived, and the resulting freedom is exploited to construct H 1 controllers which have a closed loop induced norm less than a prespecified value for all plants within a set, which is described in terms of the future variation of the plant. Dual results, with a possible adaptive interpretation, are also constructed. | [
1217
] | Train |
1,350 | 0 | Title: IGLUE An Instance-based Learning System over Lattice Theory
Abstract: Concept learning is one of the most studied areas in machine learning. A lot of work in this domain deals with decision trees. In this paper, we are concerned with a different kind of technique based on Galois lattices or concept lattices. We present a new semi-lattice based system, IGLUE, that uses the entropy function with a top-down approach to select concepts during the lattice construction. Then IGLUE generates new relevant numerical features by transforming initial boolean features over these concepts. IGLUE uses the new features to redescribe examples. Finally, IGLUE applies the Mahanalobis distance as a similarity measure between examples. Keywords : Multistrategy Learning, Instance-Based Learning, Galois lattice, Feature transformation | [
1151
] | Test |
1,351 | 1 | Title: Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification
Abstract: This paper introduces a hybrid learning methodology that integrates genetic algorithms (GAs) and decision tree learning (ID3) in order to evolve optimal subsets of discriminatory features for robust pattern classification. A GA is used to search the space of all possible subsets of a large set of candidate discrimination features. For a given feature subset, ID3 is invoked to produce a decision tree. The classification performance of the decision tree on unseen data is used as a measure of fitness for the given feature set, which, in turn, is used by the GA to evolve better feature sets. This GA-ID3 process iterates until a feature subset is found with satisfactory classification performance. Experimental results are presented which illustrate the feasibility of our approach on difficult problems involving recognizing visual concepts in satellite and facial image data. The results also show improved classification performance and reduced description complexity when compared against standard methods for feature selection. | [
900,
1498
] | Validation |
1,352 | 2 | Title: FONN: Combining First Order Logic with Connectionist Learning
Abstract: This paper presents a neural network architecture that can manage structured data and refine knowledge bases expressed in a first order logic language. The presented framework is well suited to classification problems in which concept de scriptions depend upon numerical features of the data. In fact, the main goal of the neural architecture is that of refining the numerical part of the knowledge base, without changing its structure. In particular, we discuss a method to translate a set of classification rules into neural computation units. Here, we focus our attention on the translation method and on algorithms to refine network weights on struc tured data. The classification theory to be refined can be manually handcrafted or automatically acquired by a symbolic relational learning system able to deal with numerical features. As a matter of fact, the primary goal is to bring into a neural network architecture the capability of dealing with structured data of unrestricted size, by allowing to dynamically bind the classification rules to different items occur ring in the input data. An extensive experimentation on a challenging artificial case study shows that the network converges quite fastly and generalizes much better than propositional learners on an equivalent task definition. | [
611,
1672,
2674
] | Test |
1,353 | 1 | Title: Culling Teaching -1 Culling and Teaching in Neuro-evolution
Abstract: The evolving population of neural nets contains information not only in terms of genes, but also in the collection of behaviors of the population members. Such information can be thought of as a kind of culture of the population. Two ways of exploiting that culture are explored in this paper: (1) Culling overlarge litters: Generate a large number of offspring with different crossovers, quickly evaluate them by comparing their performance to the population, and throw away those that appear poor. (2) Teaching: Use backpropagation to train offspring toward the performance of the population. Both techniques result in faster, more effective neuro-evolution, and they can be effectively combined, as is demonstrated on the inverted pendulum problem. Additional methods of cultural exploitation are possible and will be studied in future work. These results suggest that cultural exploitation is a powerful idea that allows leveraging several aspects of the genetic algorithm. | [
294,
934,
2302,
2317
] | Test |
1,354 | 0 | Title: The Structure-Mapping Engine: Algorithm and Examples
Abstract: This paper describes the Structure-Mapping Engine (SME), a program for studying analogical processing. SME has been built to explore Gentner's Structure-mapping theory of analogy, and provides a "tool kit" for constructing matching algorithms consistent with this theory. Its flexibility enhances cognitive simulation studies by simplifying experimentation. Furthermore, SME is very efficient, making it a useful component in machine learning systems as well. We review the Structure-mapping theory and describe the design of the engine. We analyze the complexity of the algorithm, and demonstrate that most of the steps are polynomial, typically bounded by O (N 2 ). Next we demonstrate some examples of its operation taken from our cognitive simulation studies and work in machine learning. Finally, we compare SME to other analogy programs and discuss several areas for future work. This paper appeared in Artificial Intelligence, 41, 1989, pp 1-63. For more information, please contact forbus@ils.nwu.edu | [
75,
313,
479,
806,
911,
992,
994,
1001,
1039,
1040,
1047,
1089,
1123,
1176,
1188,
1304,
1317,
1420,
1426,
1465,
1674,
1680,
1695
] | Train |
1,355 | 0 | Title: Modeling Invention by Analogy in ACT-R
Abstract: We investigate some aspects of cognition involved in invention, more precisely in the invention of the telephone by Alexander Graham Bell. We propose the use of the Structure-Behavior-Function (SBF) language for the representation of invention knowledge; we claim that because SBF has been shown to support a wide range of reasoning about physical devices, it constitutes a plausible account of how an inventor might represent knowledge of an invention. We further propose the use of the ACT-R architecture for the implementation of this model. ACT-R has been shown to very precisely model a wide range of human cognition. We draw upon the architecture for execution of productions and matching of declarative knowledge through spreading activation. Thus we present a model which combines the well-established cognitive validity of ACT-R with the powerful, specialized model-based reasoning methods facilitated by SBF. | [
1047,
1148,
1640,
1648
] | Train |
1,356 | 2 | Title: Constraint Tangent Distance for On-line Character Recognition
Abstract: In on-line character recognition we can observe two kinds of intra-class variations: small geometric deformations and completely different writing styles. We propose a new approach to deal with these problems by defining an extension of tangent distance [9], well known in off-line character recognition. The system has been implemented with a k-nearest neighbor classifier and a so called diabolo classifier [6] respectively. Both classifiers are invariant under transformations like rotation, scale or slope and can deal with variations in stroke order and writing direction. Results are presented for our digit database with more than 200 writers. | [
667,
1430
] | Train |
1,357 | 3 | Title: Decimatable Boltzmann Machines vs. Gibbs Sampling
Abstract: Exact Boltzmann learning can be done in certain restricted networks by the technique of decimation. We have enlarged the set of dec-imatable Boltzmann machines by introducing a new and more general decimation rule. We have compared solutions of a probability density estimation problem with decimatable Boltzmann machines to the results obtained by Gibbs sampling in unrestricted (non-decimatable) | [
1461,
1511
] | Train |
1,358 | 6 | Title: On the Complexity of Function Learning
Abstract: The majority of results in computational learning theory are concerned with concept learning, i.e. with the special case of function learning for classes of functions with range f0; 1g. Much less is known about the theory of learning functions with a larger range such as IN or IR. In particular relatively few results exist about the general structure of common models for function learning, and there are only very few nontrivial function classes for which positive learning results have been exhibited in any of these models. We introduce in this paper the notion of a binary branching adversary tree for function learning, which allows us to give a somewhat surprising equivalent characterization of the optimal learning cost for learning a class of real-valued functions (in terms of a max-min definition which does not involve any "learning" model). Another general structural result of this paper relates the cost for learning a union of function classes to the learning costs for the individual function classes. Furthermore, we exhibit an efficient learning algorithm for learning convex piecewise linear functions from IR d into IR. Previously, the class of linear functions from IR d into IR was the only class of functions with multi-dimensional domain that was known to be learnable within the rigorous framework of a formal model for on-line learning. Finally we give a sufficient condition for an arbitrary class F of functions from IR into IR that allows us to learn the class of all functions that can be written as the pointwise maximum of k functions from F . This allows us to exhibit a number of further nontrivial classes of functions from IR into IR for which there exist efficient learning algorithms. | [
453,
591,
1567,
1661
] | Train |
1,359 | 2 | Title: Extracting Comprehensible Concept Representations from Trained Neural Networks
Abstract: Although they are applicable to a wide array of problems, and have demonstrated good performance on a number of difficult, real-world tasks, neural networks are not usually applied to problems in which comprehensibility of the acquired concepts is important. The concept representations formed by neural networks are hard to understand because they typically involve distributed, nonlinear relationships encoded by a large number of real-valued parameters. To address this limitation, we have been developing algorithms for extracting "symbolic" concept representations from trained neural networks. We first discuss why it is important to be able to understand the concept representations formed by neural networks. We then briefly describe our approach and discuss a number of issues pertaining to comprehensibility that have arisen in our work. Finally, we discuss choices that we have made in our research to date, and open research issues that we have not yet addressed. | [
1057
] | Train |
1,360 | 6 | Title: Learning From a Consistently Ignorant Teacher
Abstract: One view of computational learning theory is that of a learner acquiring the knowledge of a teacher. We introduce a formal model of learning capturing the idea that teachers may have gaps in their knowledge. The goal of the learner is still to acquire the knowledge of the teacher, but now the learner must also identify the gaps. This is the notion of learning from a consistently ignorant teacher. We consider the impact of knowledge gaps on learning, for example, monotone DNF and d-dimensional boxes, and show that learning is still possible. Negatively, we show that knowledge gaps make learning conjunctions of Horn clauses as hard as learning DNF. We also present general results describing when known learning algorithms can be used to obtain learning algorithms using a consistently ignorant teacher. | [
1095
] | Validation |
1,361 | 6 | Title: An Efficient Method To Estimate Bagging's Generalization Error
Abstract: In bagging [Bre94a] one uses bootstrap replicates of the training set [Efr79, ET93] to try to improve a learning algorithm's performance. The computational requirements for estimating the resultant generalization error on a test set by means of cross-validation are often prohibitive; for leave-one-out cross-validation one needs to train the underlying algorithm on the order of m- times, where m is the size of the training set and is the number of replicates. This paper presents several techniques for exploiting the bias-variance decomposition [GBD92, Wol96] to estimate the generalization error of a bagged learning algorithm without invoking yet more training of the underlying learning algorithm. The best of our estimators exploits stacking [Wol92]. In a set of experiments reported here, it was found to be more accurate than both the alternative cross-validation-based estimator of the bagged algorithm's error and the cross-validation-based estimator of the underlying algorithm's error. This improvement was particularly pronounced for small test sets. This suggests a novel justification for using bagging| im proved estimation of generalization error. | [
1463
] | Train |
1,362 | 1 | Title: Towards Automatic Discovery of Building Blocks in Genetic Programming
Abstract: This paper presents an algorithm for the discovery of building blocks in genetic programming (GP) called adaptive representation through learning (ARL). The central idea of ARL is the adaptation of the problem representation, by extending the set of terminals and functions with a set of evolvable subroutines. The set of subroutines extracts common knowledge emerging during the evolutionary process and acquires the necessary structure for solving the problem. ARL supports subroutine creation and deletion. Subroutine creation or discovery is performed automatically based on the differential parent-offspring fitness and block activation. Subroutine deletion relies on a utility measure similar to schema fitness over a window of past generations. The technique described is tested on the problem of controlling an agent in a dynamic and non-deterministic environment. The automatic discovery of subroutines can help scale up the GP technique to complex problems. | [
163,
1178,
1184,
2175
] | Train |
1,363 | 6 | Title: Exact Identification of Read-once Formulas Using Fixed Points of Amplification Functions
Abstract: In this paper we describe a new technique for exactly identifying certain classes of read-once Boolean formulas. The method is based on sampling the input-output behavior of the target formula on a probability distribution that is determined by the fixed point of the formula's amplification function (defined as the probability that a 1 is output by the formula when each input bit is 1 independently with probability p). By performing various statistical tests on easily sampled variants of the fixed-point distribution, we are able to efficiently infer all structural information about any logarithmic-depth formula (with high probability). We apply our results to prove the existence of short universal identification sequences for large classes of formulas. We also describe extensions of our algorithms to handle high rates of noise, and to learn formulas of unbounded depth in Valiant's model with respect to specific distributions. Most of this research was carried out while all three authors were at MIT Laboratory for Computer Science with support provided by ARO Grant DAAL03-86-K-0171, DARPA Contract N00014-89-J-1988, NSF Grant CCR-88914428, and a grant from the Siemens Corporation. R. Schapire received additional support from AFOSR Grant 89-0506 while at Harvard University. S. Goldman is currently supported in part by a G.E. Foundation Junior Faculty Grant and NSF Grant CCR-9110108. | [
640,
672,
786,
1343,
1364,
2168,
2475,
2653
] | Train |
1,364 | 6 | Title: Learning k-term DNF Formulas with an Incomplete Membership Oracle
Abstract: We consider the problem of learning k-term DNF formulas using equivalence queries and incomplete membership queries as defined by Angluin and Slonim. We demonstrate that this model can be applied to non-monotone classes. Namely, we describe a polynomial-time algorithm that exactly identifies a k-term DNF formula with a k-term DNF hypothesis using incomplete membership queries and equivalence queries from the class of DNF formulas. | [
1003,
1004,
1343,
1363,
1469,
1705
] | Validation |
1,365 | 2 | Title: Word Perfect Corp. A TRANSFORMATION FOR IMPLEMENTING NEURAL NETWORKS WITH LOCALIST PROPERTIES
Abstract: Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and typically suffer from a number of shortcomings as a result. Variations of ANNs that use dynamic topologies have shown ability to overcome many of these problems. This paper introduces Location-Independent Transformations (LITs) as a general strategy for implementing feedforward networks that use dynamic topologies. A LIT creates a set of location-independent nodes, where each node computes its part of the network output independent of other nodes, using local information. This type of transformation allows efficient support for adding and deleting nodes dynamically during learning. In particular, this paper presents LITs for the single-layer competitve learning network, and the counterpropagation network, which combines elements of supervised learning with competitive learning. These two networks are localist in the sense that ultimately one node is responsible for each output. LITs for other models are presented in other papers. | [
809,
812,
814,
1044
] | Validation |
1,366 | 2 | Title: ``Learning Local Error Bars for Nonlinear Regression.'' Learning Local Error Bars for Nonlinear Regression
Abstract: We present a new method for obtaining local error bars for nonlinear regression, i.e., estimates of the confidence in predicted values that depend on the input. We approach this problem by applying a maximum-likelihood framework to an assumed distribution of errors. We demonstrate our method first on computer-generated data with locally varying, normally distributed target noise. We then apply it to laser data from the Santa Fe Time Series Competition where the underlying system noise is known quantization error and the error bars give local estimates of model misspecification. In both cases, the method also provides a weighted-regression effect that improves generalization performance. | [
1373,
2239,
2373,
2374,
2413,
2414,
2513,
2562
] | Train |
1,367 | 0 | Title: Learning to Refine Indexing by Introspective Reasoning
Abstract: A significant problem for case-based reasoning (CBR) systems is deciding what features to use in judging case similarity for retrieval. We describe research that addresses the feature selection problem by using introspective reasoning to learn new features for indexing. Our method augments the CBR system with an introspective reasoning component which monitors system performance to detect poor retrievals, identifies features which would lead to retrieving cases requiring less adaptation, and refines the indices to include such features in order to avoid similar future failures. We explore the benefit of introspective reasoning by performing empirical tests on the implemented system. These tests examine the benefits of introspective index refinement and the effects of problem order on case and index learning, and show that introspective learning of new index features improves overall performance across the range of different problem orders. | [
817
] | Train |
1,368 | 0 | Title: Structure oriented case retrieval
Abstract: | [
454,
991
] | Train |
1,369 | 1 | Title: STRUCTURAL LEARNING OF FUZZY RULES FROM NOISED EXAMPLES
Abstract: Inductive learning algorithms try to obtain the knowledge of a system from a set of examples. One of the most difficult problems in machine learning consists in getting the structure of this knowledge. We propose an algorithm able to manage with fuzzy information and able to learn the structure of the rules that represent the system. The algorithm gives a reasonable small set of fuzzy rules that represent the original set of examples. | [
1333
] | Test |
1,370 | 5 | Title: From Theory Refinement to KB Maintenance: a Position Statement
Abstract: Since we consider theory refinement (TR) as a possible key concept for a methodologically clear view of knowledge-base maintenance, we try to give a structured overview about the actual state-of-the-art in TR. This overview is arranged along the description of TR as a search problem. We explain the basic approach, show the variety of existing systems and try to give some hints about the direction future research should go. | [
136,
1102,
2692
] | Train |
1,371 | 1 | Title: Self-Nonself Discrimination in a Computer
Abstract: The problem of protecting computer systems can be viewed generally as the problem of learning to distinguish self from other. We describe a method for change detection which is based on the generation of T cells in the immune system. Mathematical analysis reveals computational costs of the system, and preliminary experiments illustrate how the method might be applied to the problem of computer viruses. | [
1114
] | Train |
1,372 | 3 | Title: MCMC CONVERGENCE DIAGNOSTIC VIA THE CENTRAL LIMIT THEOREM
Abstract: Markov Chain Monte Carlo (MCMC) methods, as introduced by Gelfand and Smith (1990), provide a simulation based strategy for statistical inference. The application fields related to these methods, as well as theoretical convergence properties, have been intensively studied in the recent literature. However, many improvements are still expected to provide workable and theoretically well-grounded solutions to the problem of monitoring the convergence of actual outputs from MCMC algorithms (i.e. the convergence assessment problem). In this paper, we introduce and discuss a methodology based on the Central Limit Theorem for Markov chains to assess convergence of MCMC algorithms. Instead of searching for approximate stationarity, we primarily intend to control the precision of estimates of the invariant probability measure, or of integrals of functions with respect to this measure, through confidence regions based on normal approximation. The first proposed control method tests the normality hypothesis for normalized averages of functions of the Markov chain over independent parallel chains. This normality control provides good guarantees that the whole state space has been explored, even in multimodal situations. It can lead to automated stopping rules. A second tool connected with the normality control is based on graphical monitoring of the stabilization of the variance after n iterations near the limiting variance appearing in the CLT. Both methods require no knowledge of the sampler driving the chain. In this paper, we mainly focus on finite state Markov chains, since this setting allows us to derive consistent estimates of both the limiting variance and the variance after n iterations. Heuristic procedures based on Berry-Esseen bounds are investigated. An extension to the continuous case is also proposed. Numerical simulations illustrating the performance of these methods are given for several examples: a finite chain with multimodal invariant probability, a finite state random walk for which the theoretical rate of convergence to stationarity is known, and a continuous state chain with multimodal invariant probability issued from a Gibbs sampler. | [
352,
896,
904
] | Train |
1,373 | 2 | Title: Direct Multi-Step Time Series Prediction Using TD()
Abstract: This paper explores the application of Temporal Difference (TD) learning (Sutton, 1988) to forecasting the behavior of dynamical systems with real-valued outputs (as opposed to game-like situations). The performance of TD learning in comparison to standard supervised learning depends on the amount of noise present in the data. In this paper, we use a deterministic chaotic time series from a low-noise laser. For the task of direct five-step ahead predictions, our experiments show that standard supervised learning is better than TD learning. The TD algorithm can be viewed as linking adjacent predictions. A similar effect can be obtained by sharing the internal representation in the network. We thus compare two architectures for both paradigms: the first architecture (separate hidden units) consists of individual networks for each of the five direct multi-step prediction tasks, the second (shared hidden units) has a single (larger) hidden layer that finds a representation from which all five predictions for the next five steps are generated. For this data set we do not find any significant difference between the two architectures. fl http://www.cs.colorado.edu/~andreas/Home.html. This paper is available as ftp://ftp.cs.colorado.edu/pub/Time-Series/MyPapers/kazlas.weigend nips7.ps.Z | [
565,
1366,
1718
] | Train |
1,374 | 2 | Title: A simple algorithm that discovers efficient perceptual codes
Abstract: We describe the "wake-sleep" algorithm that allows a multilayer, unsupervised, neural network to build a hierarchy of representations of sensory input. The network has bottom-up "recognition" connections that are used to convert sensory input into underlying representations. Unlike most artificial neural networks, it also has top-down "generative" connections that can be used to reconstruct the sensory input from the representations. In the "wake" phase of the learning algorithm, the network is driven by the bottom-up recognition connections and the top-down generative connections are trained to be better at reconstructing the sensory input from the representation chosen by the recognition process. In the "sleep" phase, the network is driven top-down by the generative connections to produce a fantasized representation and a fantasized sensory input. The recognition connections are then trained to be better at recovering the fantasized representation from the fantasized sensory input. In both phases, the synaptic learning rule is simple and local. The combined effect of the two phases is to create representations of the sensory input that are efficient in the following sense: On average, it takes more bits to describe each sensory input vector directly than to first describe the representation of the sensory input chosen by the recognition process and then describe the difference between the sensory input and its reconstruction from the chosen representation. | [
869,
1548
] | Train |
1,375 | 3 | Title: Priors for Infinite Networks
Abstract: Technical Report CRG-TR-94-1 Department of Computer Science University of Toronto 10 King's College Road Toronto, Canada M5S 1A4 Abstract Bayesian inference begins with a prior distribution for model parameters that is meant to capture prior beliefs about the relationship being modeled. For multilayer perceptron networks, where the parameters are the connection weights, the prior lacks any direct meaning | what matters is the prior over functions computed by the network that is implied by this prior over weights. In this paper, I show that priors over weights can be defined in such a way that the corresponding priors over functions reach reasonable limits as the number of hidden units in the network goes to infinity. When using such priors, there is thus no need to limit the size of the network in order to avoid "overfitting". The infinite network limit also provides insight into the properties of different priors. A Gaussian prior for hidden-to-output weights results in a Gaussian process prior for functions, which can be smooth, Brownian, or fractional Brownian, depending on the hidden unit activation function and the prior for input-to-hidden weights. Quite different effects can be obtained using priors based on non-Gaussian stable distributions. In networks with more than one hidden layer, a combination of Gaussian and non-Gaussian priors appears most interesting. | [
157,
560,
1452
] | Validation |
1,376 | 4 | Title: Near-Optimal Performance for Reinforcement Learning in Polynomial Time
Abstract: We present new algorithms for reinforcement learning and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decision processes. After observing that the number of actions required to approach the optimal return is lower bounded by the mixing time T of the optimal policy (in the undiscounted case) or by the horizon time T (in the discounted case), we then give algorithms requiring a number of actions and total computation time that are only polynomial in T and the number of states, for both the undiscounted and discounted cases. An interesting aspect of our algorithms is their explicit handling of the Exploration-Exploitation trade-off. These are the first results in the reinforcement learning literature giving algorithms that provably converge to near-optimal performance in polynomial time for general Markov decision processes. | [
306,
565,
738,
1546,
1727
] | Train |
1,377 | 0 | Title: The case for cases: a call for purity in case-based reasoning inherently more difficult than
Abstract: A basic premise of case-based reasoning (CBR) is that it involves reasoning from cases, which are representations of real episodes, rather than from rules, which are facts and if then structures with no stated connection to any real episodes. In fact, most CBR systems do not reason directly from cases | rather they reason from abstractions or simplifications of cases. In this paper, we argue for "pure" case-based reasoning, i.e., reasoning from representations that are both concrete and reasonably complete. We claim that working from representations that satisfy these criteria We illustrate our argument with examples from three previous systems, chef, swale, and hypo, as well as from cookie, a CBR system being developed by the first author. | [
288,
313,
1642
] | Train |
1,378 | 4 | Title: Generalization in Reinforcement Learning: Safely Approximating the Value Function
Abstract: To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to the curse of dimensionality in reinforcement learning and dynamic programming is to replace the lookup table with a generalizing function approximator such as a neural net. Although this has been successful in the domain of backgammon, there is no guarantee of convergence. In this paper, we show that the combination of dynamic programming and function approximation is not robust, and in even very benign cases, may produce an entirely wrong policy. We then introduce Grow-Support, a new algorithm which is safe from divergence yet can still reap the benefits of successful generalization. | [
21,
82,
173,
239,
559,
565,
575,
882,
970,
1440,
1540,
2485
] | Train |
1,379 | 1 | Title: Modeling Simple Genetic Algorithms for Permutation Problems
Abstract: An exact model of a simple genetic algorithm is developed for permutation based representations. Permutation based representations are used for scheduling problems and combinatorial problems such as the Traveling Salesman Problem. A remapping function is developed to remap the model to all permutations in the search space. The mixing matrices for various permutation based operators are also developed. | [
163,
1611
] | Train |
1,380 | 1 | Title: Evaluating Evolutionary Algorithms
Abstract: Test functions are commonly used to evaluate the effectiveness of different search algorithms. However, the results of evaluation are as dependent on the test problems as they are on the algorithms that are the subject of comparison. Unfortunately, developing a test suite for evaluating competing search algorithms is difficult without clearly defined evaluation goals. In this paper we discuss some basic principles that can be used to develop test suites and we examine the role of test suites as they have been used to evaluate evolutionary search algorithms. Current test suites include functions that are easily solved by simple search methods such as greedy hill-climbers. Some test functions also have undesirable characteristics that are exaggerated as the dimensionality of the search space is increased. New methods are examined for constructing functions with different degrees of nonlinearity, where the interactions and the cost of evaluation scale with respect to the dimensionality of the search space. | [
163,
793,
1113,
1611,
1717
] | Train |
1,381 | 3 | Title: A Context-Sensitive Generalization of ICA
Abstract: Source separation arises in a surprising number of signal processing applications, from speech recognition to EEG analysis. In the square linear blind source separation problem without time delays, one must find an unmixing matrix which can detangle the result of mixing n unknown independent sources through an unknown n fi n mixing matrix. The recently introduced ICA blind source separation algorithm (Baram and Roth 1994; Bell and Sejnowski 1995) is a powerful and surprisingly simple technique for solving this problem. ICA is all the more remarkable for performing so well despite making absolutely no use of the temporal structure of its input! This paper presents a new algorithm, contextual ICA, which derives from a maximum likelihood density estimation formulation of the problem. cICA can incorporate arbitrarily complex adaptive history-sensitive source models, and thereby make use of the temporal structure of its input. This allows it to separate in a number of situations where standard ICA cannot, including sources with low kurtosis, colored gaussian sources, and sources which have gaussian histograms. Since ICA is a special case of cICA, the MLE derivation provides as a corollary a rigorous derivation of classic ICA. | [
570,
576,
1524
] | Test |
1,382 | 2 | Title: AN ADAPTIVE NEURAL NETWORK PARSER
Abstract: We inv estigate the applicability of an adaptive neural network to problems with time-dependent input by demonstrating that a deterministic parser for natural language inputs of significant syntactic complexity can be developed using recurrent connectionist architectures. The traditional stacking mechanism, known to be necessary for proper treatment of context-free languages in symbolic systems, is absent from the design, having been subsumed by recurrency in the network. | [
1285,
1313
] | Validation |
1,383 | 2 | Title: Data-defined Problems and Multiversion Neural-net Systems
Abstract: We inv estigate the applicability of an adaptive neural network to problems with time-dependent input by demonstrating that a deterministic parser for natural language inputs of significant syntactic complexity can be developed using recurrent connectionist architectures. The traditional stacking mechanism, known to be necessary for proper treatment of context-free languages in symbolic systems, is absent from the design, having been subsumed by recurrency in the network. | [
152,
1384,
1398
] | Validation |
1,384 | 2 | Title: Use of Methodological Diversity to Improve Neural Network Generalisation
Abstract: Littlewood and Miller [1989] present a statistical framework for dealing with coincident failures in multiversion software systems. They develop a theoretical model that holds the promise of high system reliability through the use of multiple, diverse sets of alternative versions. In this paper we adapt their framework to investigate the feasibility of exploiting the diversity observable in multiple populations of neural networks developed using diverse methodologies. We evaluate the generalisation improvements achieved by a range of methodologically diverse network generation processes. We attempt to order the constituent methodological features with respect to their potential for use in the engineering of useful diversity. We also define and explore the use of relative measures of the diversity between version sets as a guide to the potential for exploiting inter-set diversity. | [
1383
] | Test |
1,385 | 0 | Title: Learning Control Knowledge in Models of Expertise ECML'95 Workshop on Knowledge-Level Modelling and Machine Learning
Abstract: During the development and the life-cycle of knowledge-based systems the requirements on the system and the knowledge in the system will change. One of the types of knowledge affected by changing requirements is control-knowledge, which prescribes the ordering of problem-solving steps. Machine-learning can aid developers of knowledge-based systems in adapting their systems to changing requirements. A number of machine-learning techniques for learning control-knowledge have been applied to problem-solvers (Prodigy-EBL, LEX). In knowledge engineering, the focus has shifted to the construction of knowledge-level models of problem-solving instead of directly constructing a knowledge-based system in a problem-solver. In this paper we describe work in progress on how to apply machine learning techniques to the KADS model of expertise. | [
1653,
1706
] | Train |
1,386 | 6 | Title: New Evidence Driven State Merging Algorithm
Abstract: Results of the Abbadingo One DFA Learning Competition Abstract This paper first describes the structure and results of the Abbadingo One DFA Learning Competition. The competition was designed to encourage work on algorithms that scale wellboth to larger DFAs and to sparser training data. We then describe and discuss the winning algorithm of Rodney Price, which orders state merges according to the amount of evidence in their favor. A second winning algorithm, of Hugues and | [
672,
1715,
2360
] | Train |
1,387 | 2 | Title: Cortical activity flips among quasi stationary states
Abstract: M. Abeles, H. Bergman and E. Vaadia, School of Medicine and Center for Neural Computation Hebrew University, POB 12272, Jerusalem 91120, Is-rael. E. Seidemann and I. Meilijson, School of Mathematical Sciences, Raymond and Beverly Sackler Faculty of Exact Sciences, and School of Medicine, Tel Aviv University, 69978 Tel Aviv, Israel. I. Gat and N. Tishby, Institute of Computer Science and Center for Neural Computation, Hebrew University, Jerusalem 91904, Israel. | [
1239
] | Train |
1,388 | 6 | Title: A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization
Abstract: In this work, we present a new bottom-up algorithm for decision tree pruning that is very efficient (requiring only a single pass through the given tree), and prove a strong performance guarantee for the generalization error of the resulting pruned tree. We work in the typical setting in which the given tree T may have been derived from the given training sample S, and thus may badly overfit S. In this setting, we give bounds on the amount of additional generalization error that our pruning suffers compared to the optimal pruning of T . More generally, our results show that if there is a pruning of T with small error, and whose size is small compared to jSj, then our algorithm will find a pruning whose error is not much larger. This style of result has been called an index of resolvability result by Barron and Cover in the context of density estimation. A novel feature of our algorithm is its locality | the decision to prune a subtree is based entirely on properties of that subtree and the sample reaching it. To analyze our algorithm, we develop tools of local uniform convergence, a generalization of the standard notion that may prove useful in other settings. | [
848,
1025,
1027,
1586
] | Train |
1,389 | 2 | Title: Support Vector Machines: Training and Applications
Abstract: The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Laboratories [3, 6, 8, 24]. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. The main idea behind the technique is to separate the classes with a surface that maximizes the margin between them. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle [23]. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Since Structural Risk Minimization is an inductive principle that aims at minimizing a bound on the generalization error of a model, rather than minimizing the Mean Square Error over the data set (as Empirical Risk Minimization methods do), training a SVM to obtain the maximum margin classifier requires a different objective function. This objective function is then optimized by solving a large-scale quadratic programming problem with linear and box constraints. The problem is considered challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM's over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM's, we present preliminary results in Frontal Human Face Detection in images. This application opens many interesting questions and future research opportunities, both in the context of faster and better optimization algorithms, and in the use of SVM's in other pattern classification, recognition, and detection applications. This report describes research done within the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences and the Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. This research is sponsored by MURI grant N00014-95-1-0600; by a grant from ONR/ARPA under contract N00014-92-J-1879 and by the National Science Foundation under contract ASC-9217041 (this award includes funds from ARPA provided under the HPCC program). Edgar Osuna was supported by Fundacion Gran Mariscal de Ayacucho and Daimler Benz. Additional support is provided by Daimler-Benz, Eastman Kodak Company, Siemens Corporate Research, Inc. and AT&T. | [
821,
1050,
1079,
2707
] | Test |
1,390 | 6 | Title: Learning Finite Automata Using Local Distinguishing Experiments
Abstract: One of the open problems listed in [ Rivest and Schapire, 1989 ] is whether and how that the copies of L fl in their algorithm can be combined into one for better performance. This paper describes an algorithm called D fl that does that combination. The idea is to represent the states of the learned model using observable symbols as well as hidden symbols that are constructed during learning. These hidden symbols are created to reflect the distinct behaviors of the model states. The distinct behaviors are represented as local distinguishing experiments (LDEs) (not to be confused with global distinguishing sequences), and these LDEs are created when the learner's prediction mismatches the actual observation from the unknown machine. To synchronize the model with the environment, these LDEs can also be concatenated to form a homing sequence. It can be shown that D fl can learn, with probability 1 , a model that is an *-approximation of the unknown machine, in a number of actions polynomial in the size of the environment and | [
1491
] | Train |
1,391 | 1 | Title: Models in Evolutionary Ecology and the Validation Problem Caswell for their guidance and support. evolutionary
Abstract: All models of natural systems represent an abstraction and simplification of that system. Thus all models suffer a validation problem. Should we believe that the results of the model have any bearing on reality? This is a particularly acute problem for Alife models of evolution and ecosystems. The time scale of evolution and the complexity of ecosystems make controlled experiments difficult. If Alife is ever to contribute significantly to biology, we must find methods by which we can build confidence in our models. One alternative to experimental tests of a model is to validate it against previously verified theory. I have applied a series of ecological and evolutionary validation tests to a model of species diversification. Examination of the predator-prey dynamics, trophic cascades, competitive exclusion, adaptation, and the species-area curve in the model has shown that a course grained spatial structure was inadequate to capture the realistic dynamics of an ecosystem. Only when spatial structure was extended to the local patch dynamics did the model begin to behave realistically under a wide range of parameters. Validation of the ecological dynamics of the model provides indirect support for the evolutionary behavior of the species within the ecosystem. Every model is an abstraction and a simplification. The goal of a model is to capture the essence of a system in the real world such that the behavior of the model matches the behavior of the real system. Thus for any model we may ask if it is a valid representation of the real system. Answering this question is the problem of validation. Traditionally we can try to disprove the validity of the model by collecting data from the real system and comparing it to the predictions of the model. In artificial life we rarely have that luxury. Artificial life models tend to be highly abstract and general because the field is striving to discover general properties of life. This makes experimental validation extremely difficult. The time scale of evolution tends to restrict experiments to observation of the fossil record (Benton 1990, for example) or manipulation of organisms with extremely short life-cycles in simplified environments (Krukonis 1996, for example). Similarly, the complexity and size of ecosystems makes ecological experiments cumbersome and difficult to control. An alternative form of validation can be pursued indirectly through reference to ecological and evolutionary theory. Instead of asking if the model matches the experimental data, we can ask if the model matches our understanding of the dynamics of ecology and evolution. Then, to the extent that the theories of ecology and evolution have been validated by experimental observations, we can disprove the validity of a model when it fails to match those theories. What follows is an example of this technique applied to a model designed to examine the factors that impact the origin and maintenance of species diversity. While the purpose of this model is to explore new theoretical ground in biology, the ecological and evolutionary dynamics in the model have been validated against theories of predation, competition, adaptation and island biogeography. Hraber and Milne (1997) looked at genotype diversity under the presence or absence of selection and varying mutation rates in the ECHO model (Holland 1992; 1993). Mirroring Bedau et al.'s (1992) results, they found that genotypic diversity was greatest under | [
1319
] | Validation |
1,392 | 1 | Title: Adapting Crossover in Evolutionary Algorithms
Abstract: One of the issues in evolutionary algorithms (EAs) is the relative importance of two search operators: mutation and crossover. Genetic algorithms (GAs) and genetic programming (GP) stress the role of crossover, while evolutionary programming (EP) and evolution strategies (ESs) stress the role of mutation. The existence of many different forms of crossover further complicates the issue. Despite theoretical analysis, it appears difficult to decide a priori which form of crossover to use, or even if crossover should be used at all. One possible solution to this difficulty is to have the EA be self-adaptive, i.e., to have the EA dynamically modify which forms of crossover to use and how often to use them, as it solves a problem. This paper describes an adaptive mechanism for controlling the use of crossover in an EA and explores the behavior of this mechanism in a number of different situations. An improvement to the adaptive mechanism is then presented. Surprisingly this improvement can also be used to enhance performance in a non-adaptive EA. | [
1299
] | Test |
1,393 | 3 | Title: Probabilistic Independence Networks for Hidden Markov Probability Models
Abstract: Graphical techniques for modeling the dependencies of random variables have been explored in a variety of different areas including statistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics. Formalisms for manipulating these models have been developed relatively independently in these research communities. In this paper we explore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper contains a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general inference algorithms for arbitrary PINs. Furthermore, the existence of inference and estimation algorithms for more general graphical models provides a set of analysis tools for HMM practitioners who wish to explore a richer class of HMM structures. Examples of relatively complex models to handle sensor fusion and coarticulation in speech recognition are introduced and treated within the graphical model framework to illustrate the advantages of the general approach. | [
905,
976,
1097,
1128,
1397,
1414,
1437,
1502
] | Train |
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