node_id int64 0 76.9k | label int64 0 39 | text stringlengths 13 124k | neighbors listlengths 0 3.32k | mask stringclasses 4
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|---|---|---|---|---|
2,494 | 4 | Title: Incremental Pruning: A Simple, Fast, Exact Algorithm for Partially Observable Markov Decision Processes
Abstract: Most exact algorithms for general pomdps use a form of dynamic programming in which a piecewise-linear and convex representation of one value function is transformed into another. We examine variations of the "incremental pruning" approach for solving this problem and compare them to earlier algorithms from theoretical and empirical perspectives. We find that incremental pruning is presently the most efficient algorithm for solving pomdps. | [
2063
] | Train |
2,495 | 6 | Title: Similar Classifiers and VC Error Bounds
Abstract: We improve error bounds based on VC analysis for classes with sets of similar classifiers. We apply the new error bounds to separating planes and artificial neural networks. Key words machine learning, learning theory, generalization, Vapnik-Chervonenkis, separating planes, neural networks. | [
58,
571,
1762,
2694
] | Train |
2,496 | 2 | Title: Gene Structure Prediction by Linguistic Methods
Abstract: The higher-order structure of genes and other features of biological sequences can be described by means of formal grammars. These grammars can then be used by general-purpose parsers to detect and assemble such structures by means of syntactic pattern recognition. We describe a grammar and parser for eukaryotic protein-encoding genes, which by some measures is as effective as current connectionist and combinatorial algorithms in predicting gene structures for sequence database entries. Parameters on the grammar rules are optimized for several different species, and mixing experiments performed to determine the degree of species specificity and the relative importance of compositional, signal-based, and syntactic components in gene prediction. | [
613,
2107,
2571
] | Test |
2,497 | 2 | Title: Learning a Specialization for Face Recognition: The Effect of Spatial Frequency
Abstract: The double dissociation between prosopagnosia, a face recognition deficit occurring after brain damage, and visual object agnosia, difficulty recognizing other kinds of complex objects, indicates that face and non-face object recognition may be served by partially independent mechanisms in the brain. Such a dissociation could be the result of a competitive learning mechanism that, during development, devotes neural resources to the tasks they are best at performing. Studies of normal adult performance on face and object recognition tasks seem to indicate that face recognition is primarily configural, involving the low spatial frequency information present in a stimulus over relatively large distances, whereas object recognition is primarily featural, involving analysis of the object's parts using local, high spatial frequency information. In a feed-forward computational model of visual processing, two modules compete to classify input stimuli; when one module receives low spatial frequency information and the other receives high spatial frequency information, the low-frequency module shows a strong specialization for face recognition in a combined face identification/object classification task. The series of experiments shows that the fine discrimination necessary for distinguishing members of a visually homoge neous class such as faces relies heavily on the low spatial frequencies present in a stimulus. | [
1915
] | Train |
2,498 | 2 | Title: Combining Exploratory Projection Pursuit And Projection Pursuit Regression With Application To Neural Networks
Abstract: We present a novel classification and regression method that combines exploratory projection pursuit (unsupervised training) with projection pursuit regression (supervised training), to yield a new family of cost/complexity penalty terms. Some improved generalization properties are demonstrated on real world problems. | [
359,
2147,
2322,
2499,
2500,
2567
] | Test |
2,499 | 2 | Title: Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions
Abstract: In this paper, we present an objective function formulation of the BCM theory of visual cortical plasticity that permits us to demonstrate the connection between the unsupervised BCM learning procedure and various statistical methods, in particular, that of Projection Pursuit. This formulation provides a general method for stability analysis of the fixed points of the theory and enables us to analyze the behavior and the evolution of the network under various visual rearing conditions. It also allows comparison with many existing unsupervised methods. This model has been shown successful in various applications such as phoneme and 3D object recognition. We thus have the striking and possibly highly significant result that a biological neuron is performing a sophisticated statistical procedure. | [
203,
359,
863,
1068,
1418,
1787,
1871,
1935,
2147,
2322,
2357,
2376,
2385,
2422,
2498,
2500,
2505
] | Train |
2,500 | 2 | Title: Face Recognition using a Hybrid Supervised/Unsupervised Neural Network
Abstract: A system for automatic face recognition is presented. It consists of several steps; Automatic detection of the eyes and mouth is followed by a spatial normalization of the images. The classification of the normalized images is carried out by a hybrid (supervised and unsupervised) Neural Network. Two methods for reducing the overfitting a common problem in high dimensional classification schemes are presented, and the superiority of their combination is demonstrated. | [
1068,
2322,
2376,
2422,
2498,
2499
] | Validation |
2,501 | 2 | Title: EMRBF: A Statistical Basis for Using Radial Basis Functions for Process Control
Abstract: Radial Basis Function (RBF) neural networks offer an attractive equation form for use in model-based control because they can approximate highly nonlinear plants and yet are well suited for linear adaptive control. We show how interpreting RBFs as mixtures of Gaussians allows the application of many statistical tools including the EM algorithm for parameter estimation. The resulting EMRBF models give uncertainty estimates and warn when they are extrapolating beyond the region where training data was available. | [
611,
2260
] | Train |
2,502 | 0 | Title: Modeling Ill-Structured Optimization Tasks through Cases
Abstract: CABINS is a framework of modeling an optimization task in ill-structured domains. In such domains, neither systems nor human experts possess the exact model for guiding optimization. And the user's model of optimality is subjective and situation-dependent. CABINS optimizes a solution through iterative revision using case-based reasoning. In CABINS, task structure analysis was adopted for creating an initial model of the optimization task. Generic vocabularies found in the analysis were specialized into case feature descriptions for application problems. Extensive experimentation on job shop scheduling problems has shown that CABINS can operationalize and improve the model through the accumulation of cases. | [
717,
2605
] | Train |
2,503 | 2 | Title: `Balancing' of conductances may explain irregular cortical spiking.
Abstract: Five related factors are identified which enable single compartment Hodgkin-Huxley model neurons to convert random synaptic input into irregular spike trains similar to those seen in in vivo cortical recordings. We suggest that cortical neurons may operate in a narrow parameter regime where synaptic and intrinsic conductances are balanced to re flect, through spike timing, detailed correlations in the inputs. fl Please send comments to tony@salk.edu. The reference for this paper is: Technical Report no. INC-9502, February 1995, Institute for Neural Computation, UCSD, San Diego, CA 92093-0523. | [
2358
] | Train |
2,504 | 1 | Title: Genetic Encoding Strategies for Neural Networks
Abstract: The application of genetic algorithms to neural network optimization (GANN) has produced an active field of research. This paper proposes a classification of the encoding strategies and it also gives a critical analysis of the current state of development. The idea of evolving artificial neural networks (NN) by genetic algorithms (GA) is based on a powerful metaphor: the evolution of the human brain. This mechanism has developed the highest form of intelligence known from scratch. The metaphor has inspired a great deal of research activities that can be traced to the late 1980s (for instance [15]). An increasing amount of research reports, journal papers and theses have been published on the topic, generating a conti-nously growing field. Researchers have devoloped a variety of different techniques to encode neural networks for the GA, with increasing complexity. This young field is driven mostly by small, independet research groups that scarcely cooperate with each other. This paper will attempt to analyse and to structure the already performed work, and to point out the shortcomings of the approaches. | [
1536,
1663,
2667
] | Train |
2,505 | 2 | Title: Three-Dimensional Object Recognition Using an Unsupervised BCM Network: The Usefulness of Distinguishing Features
Abstract: We propose an object recognition scheme based on a method for feature extraction from gray level images that corresponds to recent statistical theory, called projection pursuit, and is derived from a biologically motivated feature extracting neuron. To evaluate the performance of this method we use a set of very detailed psychophysical 3D object recognition experiments (Bulthoff and Edelman, 1992). | [
359,
611,
2499
] | Test |
2,506 | 3 | Title: Nonlinear wavelet shrinkage with Bayes rules and Bayes factors 1
Abstract: Wavelet shrinkage,the method proposed by seminal work of Donohoand Johnstone is a disarmingly simple and efficient way of de-noising data. Shrinking wavelet coefficients was proposed from several optimality criteria. The most notable are the asymptotic minimax and cross-validation criteria. In this paper a wavelet shrinkage by imposing natural properties of Bayesian models on data is proposed. The performance of methods are tested on standard Donoho-Johnstone test functions. Key Words and Phrases: Wavelets, Discrete Wavelet Transform, Thresholding, Bayes Model. 1991 AMS Subject Classification: 42A06, 62G07. | [
1910,
2366,
2416,
2458,
2575,
2661
] | Train |
2,507 | 2 | Title: The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING
Abstract: This paper introduces the idea of clearning, of simultaneously cleaning data and learning the underlying structure. The cleaning step can be viewed as top-down processing (the model modifies the data), and the learning step can be viewed as bottom-up processing (where the data modifies the model). After discussing the statistical foundation of the proposed method from a maximum likelihood perspective, we apply clearning to a notoriously hard problem where benchmark performances are very well known: the prediction of foreign exchange rates. On the difficult 1993-1994 test period, clearning in conjunction with pruning yields an annualized return between 35 and 40% (out-of-sample), significantly better than an otherwise identical network trained without cleaning. The network was started with 69 inputs and 15 hidden units and ended up with only 39 non-zero weights between inputs and hidden units. The resulting ultra-sparse final architectures obtained with clearning and pruning are immune against overfitting, even on very noisy problems since the cleaned data allow for a simpler model. Apart from the very competitive performance, clearning gives insight into the data: we show how to estimate the overall signal-to-noise ratio of each input variable, and we show that error estimates for each pattern can be used to detect and remove outliers, and to replace missing or corrupted data by cleaned values. Clearning can be used in any nonlinear regression or classification problem. | [
371,
2239,
2373,
2374
] | Train |
2,508 | 6 | Title: WRAPPERS FOR PERFORMANCE ENHANCEMENT AND OBLIVIOUS DECISION GRAPHS
Abstract: This paper introduces the idea of clearning, of simultaneously cleaning data and learning the underlying structure. The cleaning step can be viewed as top-down processing (the model modifies the data), and the learning step can be viewed as bottom-up processing (where the data modifies the model). After discussing the statistical foundation of the proposed method from a maximum likelihood perspective, we apply clearning to a notoriously hard problem where benchmark performances are very well known: the prediction of foreign exchange rates. On the difficult 1993-1994 test period, clearning in conjunction with pruning yields an annualized return between 35 and 40% (out-of-sample), significantly better than an otherwise identical network trained without cleaning. The network was started with 69 inputs and 15 hidden units and ended up with only 39 non-zero weights between inputs and hidden units. The resulting ultra-sparse final architectures obtained with clearning and pruning are immune against overfitting, even on very noisy problems since the cleaned data allow for a simpler model. Apart from the very competitive performance, clearning gives insight into the data: we show how to estimate the overall signal-to-noise ratio of each input variable, and we show that error estimates for each pattern can be used to detect and remove outliers, and to replace missing or corrupted data by cleaned values. Clearning can be used in any nonlinear regression or classification problem. | [
322,
632,
1235,
2137,
2577
] | Test |
2,509 | 6 | Title: Applying Winnow to Context-Sensitive Spelling Correction
Abstract: Multiplicative weight-updating algorithms such as Winnow have been studied extensively in the COLT literature, but only recently have people started to use them in applications. In this paper, we apply a Winnow-based algorithm to a task in natural language: context-sensitive spelling correction. This is the task of fixing spelling errors that happen to result in valid words, such as substituting to for too, casual for causal, and so on. Previous approaches to this problem have been statistics-based; we compare Winnow to one of the more successful such approaches, which uses Bayesian classifiers. We find that: (1) When the standard (heavily-pruned) set of features is used to describe problem instances, Winnow performs comparably to the Bayesian method; (2) When the full (unpruned) set of features is used, Winnow is able to exploit the new features and convincingly outperform Bayes; and (3) When a test set is encountered that is dissimilar to the training set, Winnow is better than Bayes at adapting to the unfamiliar test set, using a strategy we will present for combining learning on the training set with unsupervised learning on the (noisy) test set. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Information Technology Center America; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Information Technology Center America. All rights reserved. | [
517,
1962,
2618
] | Train |
2,510 | 3 | Title: Geometric Ergodicity and Hybrid Markov Chains
Abstract: Various notions of geometric ergodicity for Markov chains on general state spaces exist. In this paper, we review certain relations and implications among them. We then apply these results to a collection of chains commonly used in Markov chain Monte Carlo simulation algorithms, the so-called hybrid chains. We prove that under certain conditions, a hybrid chain will "inherit" the geometric ergodicity of its constituent parts. Acknowledgements. We thank Charlie Geyer for a number of very useful comments regarding spectral theory and central limit theorems. We thank Alison Gibbs, Phil Reiss, Peter Rosenthal, and Richard Tweedie for very helpful discussions. We thank the referee and the editor for many excellent suggestions. | [
416,
1713,
1977,
1978,
1991,
2002,
2362
] | Train |
2,511 | 6 | Title: A Faster Algorithm for the Perfect Phylogeny Problem when the number of Characters is Fixed TR94-05
Abstract: Various notions of geometric ergodicity for Markov chains on general state spaces exist. In this paper, we review certain relations and implications among them. We then apply these results to a collection of chains commonly used in Markov chain Monte Carlo simulation algorithms, the so-called hybrid chains. We prove that under certain conditions, a hybrid chain will "inherit" the geometric ergodicity of its constituent parts. Acknowledgements. We thank Charlie Geyer for a number of very useful comments regarding spectral theory and central limit theorems. We thank Alison Gibbs, Phil Reiss, Peter Rosenthal, and Richard Tweedie for very helpful discussions. We thank the referee and the editor for many excellent suggestions. | [
2083,
2141,
2320,
2345,
2418
] | Train |
2,512 | 1 | Title: A Methodology for Strategy Optimization Under Uncertainty in the Extended Two-Dimensional Pursuer/Evader Problem
Abstract: | [
1930
] | Train |
2,513 | 2 | Title: Avoiding overfitting by locally matching the noise level of the data gating network discovers the
Abstract: When trying to forecast the future behavior of a real-world system, two of the key problems are nonstationarity of the process (e.g., regime switching) and overfitting of the model (particularly serious for noisy processes). This articles shows how gated experts can point to solutions to these problems. The architecture, also called society of experts and mixture of experts consists of a (nonlinear) gating network and several (nonlinear) competing experts. Each expert learns a conditional mean (as usual), but each expert also has its own adaptive width. The gating network learns to assign a probability to each expert that depends on the input. This article first discusses the assumptions underlying this architecture and derives the weight update rules. It then evaluates the performance of gated experts in comparison to that of single networks, as well as to networks with two outputs, one predicting the mean, the other one the local error bar. This article also investigates the ability of gated experts to discover and characterize underlying the regimes. The results are: * there is significantly less overfitting compared to single nets, for two reasons: only subsets of the potential inputs are given to the experts and gating network (less of a curse of dimensionality), and the experts learn to match their variances to the (local) noise levels, thus only learning as This article focuses on the architecture and the overfitting problem. Applications to a computer-generated toy problem and the laser data from Santa Fe Competition are given in [Mangeas and Weigend, 1995], and the application to the real-world problem of predicting the electricity demand of France are given in [Mangeas et al., 1995]. much as the data support. | [
74,
310,
1366,
2239,
2374
] | Validation |
2,514 | 3 | Title: Learning Bayesian Prototype Trees by Simulated Annealing
Abstract: Given a set of samples of an unknown probability distribution, we study the problem of constructing a good approximative Bayesian network model of the probability distribution in question. This task can be viewed as a search problem, where the goal is to find a maximal probability network model, given the data. In this work, we do not make an attempt to learn arbitrarily complex multi-connected Bayesian network structures, since such resulting models can be unsuitable for practical purposes due to the exponential amount of time required for the reasoning task. Instead, we restrict ourselves to a special class of simple tree-structured Bayesian networks called Bayesian prototype trees, for which a polynomial time algorithm for Bayesian reasoning exists. We show how the probability of a given Bayesian prototype tree model can be evaluated, given the data, and how this evaluation criterion can be used in a stochastic simulated annealing algorithm for searching the model space. The simulated annealing algorithm provably finds the maximal probability model, provided that a sufficient amount of time is used. | [
485,
1838,
1908,
2380,
2558
] | Train |
2,515 | 1 | Title: Forward-Tracking: A Technique for Searching Beyond Failure
Abstract: In many applications, such as decision support, negotiation, planning, scheduling, etc., one needs to express requirements that can only be partially satisfied. In order to express such requirements, we propose a technique called forward-tracking. Intuitively, forward-tracking is a kind of dual of chronological back-tracking: if a program globally fails to find a solution, then a new execution is started from a program point and a state `forward' in the computation tree. This search technique is applied to constraint logic programming, obtaining a powerful extension that preserves all the useful properties of the original scheme. We report on the successful practical application of forward-tracking to the evolutionary training of (constrained) neural networks. | [
1999,
2003
] | Train |
2,516 | 1 | Title: When Gravity Fails: Local Search Topology
Abstract: Local search algorithms for combinatorial search problems frequently encounter a sequence of states in which it is impossible to improve the value of the objective function; moves through these regions, called plateau moves, dominate the time spent in local search. We analyze and characterize plateaus for three different classes of randomly generated Boolean Satisfiability problems. We identify several interesting features of plateaus that impact the performance of local search algorithms. We show that local minima tend to be small but occasionally may be very large. We also show that local minima can be escaped without unsatisfying a large number of clauses, but that systematically searching for an escape route may be computationally expensive if the local minimum is large. We show that plateaus with exits, called benches, tend to be much larger than minima, and that some benches have very few exit states which local search can use to escape. We show that the solutions (i.e., global minima) of randomly generated problem instances form clusters, which behave similarly to local minima. We revisit several enhancements of local search algorithms and explain their performance in light of our results. Finally we discuss strategies for creating the next generation of local search algorithms. | [
1946
] | Train |
2,517 | 2 | Title: Solving the Temporal Binding Problem: A Neural Theory for Constructing and Updating Object Files
Abstract: Visual objects are perceived only if their parts are correctly identified and integrated. A neural network theory is proposed that seeks to explain how the human visual system binds together visual properties, dispersed over space and time, of multiple objects a problem known as the temporal binding problem [49, 30]. The proposed theory is based upon neural mechanisms that construct and update object representations through the interactions of a serial attentional mechanism for location and object-based selection, preattentive Gestalt-based grouping mechanisms, and an associative memory structure that binds together object identity, form, and spatial information. A working model is presented that provides a unified quantitative explanation of results from psychophysical experiments on object review, object integration and multielement tracking. | [
2533
] | Test |
2,518 | 1 | Title: Tracing the Behavior of Genetic Algorithms Using Expected Values of Bit and Walsh Products
Abstract: We consider two methods for tracing genetic algorithms. The first method is based on the expected values of bit products and the second method on the expected values of Walsh products. We treat proportional selection, mutation and uniform and one-point crossover. As applications, we obtain results on stable points and fitness of schemata. | [
163,
2298
] | Train |
2,519 | 1 | Title: An Evolutionary Approach to Time Constrained Routing Problems
Abstract: Routing problems are an important class of planning problems. Usually there are many different constraints and optimization criteria involved, and it is difficult to find general methods for solving routing problems. We propose an evolutionary solver for such planning problems. An instance of this solver has been tested on a specific routing problem with time constraints. The performance of this evolutionary solver is compared to a biased random solver and a biased hillclimber solver. Results show that the evolutionary solver performs significantly better than the other two solvers. | [
2264
] | Validation |
2,520 | 0 | Title: Cooperative Case-Based Reasoning
Abstract: We are investigating possible modes of cooperation among homogeneous agents with learning capabilities. In this paper we will be focused on agents that learn and solve problems using Case-based Reasoning (CBR), and we will present two modes of cooperation among them: Distributed Case-based Reasoning (DistCBR) and Collective Case-based Reasoning (ColCBR). We illustrate these modes with an application where different CBR agents able to recommend chromatography techniques for protein purification cooperate. The approach taken is to extend Noos, the representation language being used by the CBR agents. Noos is knowledge modeling framework designed to integrate learning methods and based on the task/method decomposition principle. The extension we present, Plural Noos, allows communication and cooperation among agents implemented in Noos by means of three basic constructs: alien references, foreign method evaluation, and mobile methods. | [
66,
2359
] | Test |
2,521 | 1 | Title: Case-Based Probability Factoring in Bayesian Belief Networks
Abstract: Bayesian network inference can be formulated as a combinatorial optimization problem, concerning in the computation of an optimal factoring for the distribution represented in the net. Since the determination of an optimal factoring is a computationally hard problem, heuristic greedy strategies able to find approximations of the optimal factoring are usually adopted. In the present paper we investigate an alternative approach based on a combination of genetic algorithms (GA) and case-based reasoning (CBR). We show how the use of genetic algorithms can improve the quality of the computed factoring in case a static strategy is used (as for the MPE computation), while the combination of GA and CBR can still provide advantages in the case of dynamic strategies. Some preliminary results on different kinds of nets are then reported. | [
145,
163,
2164,
2529
] | Validation |
2,522 | 2 | Title: A Symbolic Complexity Analysis of Connectionist Algorithms for Distributed-Memory Machines
Abstract: This paper attempts to rigorously determine the computation and communication requirements of connectionist algorithms running on a distributed-memory machine. The strategy involves (1) specifying key connectionist algorithms in a high-level object-oriented language, (2) extracting their running times as polynomials, and (3) analyzing these polynomials to determine the algorithms' space and time complexity. Results are presented for various implementations of the back-propagation algorithm [4]. | [
2275
] | Train |
2,523 | 2 | Title: ADAPTIVE LOOK-AHEAD PLANNING problem of finding good initial plans is solved by the use of
Abstract: We present a new adaptive connectionist planning method. By interaction with an environment a world model is progressively constructed using the backpropagation learning algorithm. The planner constructs a look-ahead plan by iteratively using this model to predict future reinforcements. Future reinforcement is maximized to derive suboptimal plans, thus determining good actions directly from the knowledge of the model network (strategic level). This is done by gradient descent in action space. | [
2684
] | Train |
2,524 | 3 | Title: ADAPTIVE LOOK-AHEAD PLANNING problem of finding good initial plans is solved by the use of
Abstract: In the Proceedings of the Conference on Uncertainty in Artificial Intelli- gence (UAI-94), Seattle, WA, 46-54, July 29-31, 1994. Technical Report R-213-B April, 1994 Abstract Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [Balke and Pearl, 1994], where the antecedent of the query is interpreted as an external action that forces the proposition A to be true. When a prior probability is available on the causal mechanisms governing the domain, counterfactual probabilities can be evaluated precisely. However, when causal knowledge is specified as conditional probabilities on the observables, only bounds can computed. This paper develops techniques for evaluating these bounds, and demonstrates their use in two applications: (1) the determination of treatment efficacy from studies in which subjects may choose their own treatment, and (2) the determination of liability in product-safety litigation. | [
260,
1527,
1894,
2088,
2166
] | Validation |
2,525 | 3 | Title: Bayesian Networks
Abstract: In the Proceedings of the Conference on Uncertainty in Artificial Intelli- gence (UAI-94), Seattle, WA, 46-54, July 29-31, 1994. Technical Report R-213-B April, 1994 Abstract Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [Balke and Pearl, 1994], where the antecedent of the query is interpreted as an external action that forces the proposition A to be true. When a prior probability is available on the causal mechanisms governing the domain, counterfactual probabilities can be evaluated precisely. However, when causal knowledge is specified as conditional probabilities on the observables, only bounds can computed. This paper develops techniques for evaluating these bounds, and demonstrates their use in two applications: (1) the determination of treatment efficacy from studies in which subjects may choose their own treatment, and (2) the determination of liability in product-safety litigation. | [
1527,
2088
] | Train |
2,526 | 2 | Title: Logistic Response Projection Pursuit
Abstract: In the Proceedings of the Conference on Uncertainty in Artificial Intelli- gence (UAI-94), Seattle, WA, 46-54, July 29-31, 1994. Technical Report R-213-B April, 1994 Abstract Evaluation of counterfactual queries (e.g., "If A were true, would C have been true?") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [Balke and Pearl, 1994], where the antecedent of the query is interpreted as an external action that forces the proposition A to be true. When a prior probability is available on the causal mechanisms governing the domain, counterfactual probabilities can be evaluated precisely. However, when causal knowledge is specified as conditional probabilities on the observables, only bounds can computed. This paper develops techniques for evaluating these bounds, and demonstrates their use in two applications: (1) the determination of treatment efficacy from studies in which subjects may choose their own treatment, and (2) the determination of liability in product-safety litigation. | [
2448
] | Train |
2,527 | 5 | Title: 248 Efficient Superscalar Performance Through Boosting
Abstract: The foremost goal of superscalar processor design is to increase performance through the exploitation of instruction-level parallelism (ILP). Previous studies have shown that speculative execution is required for high instruction per cycle (IPC) rates in non-numerical applications. The general trend has been toward supporting speculative execution in complicated, dynamically-scheduled processors. Performance, though, is more than just a high IPC rate; it also depends upon instruction count and cycle time. Boosting is an architectural technique that supports general speculative execution in simpler, statically-scheduled processors. Boosting labels speculative instructions with their control dependence information. This labelling eliminates control dependence constraints on instruction scheduling while still providing full dependence information to the hardware. We have incorporated boosting into a trace-based, global scheduling algorithm that exploits ILP without adversely affecting the instruction count of a program. We use this algorithm and estimates of the boosting hardware involved to evaluate how much speculative execution support is really necessary to achieve good performance. We find that a statically-scheduled superscalar processor using a minimal implementation of boosting can easily reach the performance of a much more complex dynamically-scheduled superscalar processor. | [
735,
1956,
1961,
2100
] | Train |
2,528 | 6 | Title: The Minimum Feature Set Problem
Abstract: This paper appeared in Neural Networks 7 (1994), no. 3, pp. 491-494. | [
1858
] | Train |
2,529 | 3 | Title: Decision-Theoretic Case-Based Reasoning
Abstract: Technical Report MSR-TR-95-03 | [
2294,
2521
] | Train |
2,530 | 2 | Title: Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge
Abstract: This research is sponsored in part by the National Science Foundation under award IRI-9313367, and by the Wright Laboratory, Aeronautical Systems Center, Air Force Materiel Command, USAF, and the Advanced Research Projects Agency (ARPA) under grant number F33615-93-1-1330. The views and conclusions contained in this document are those of the author and should not be interpreted as necessarily representing official policies or endorsements, either expressed or implied, of NSF, Wright Laboratory or the United States Government. | [
2090,
2415
] | Validation |
2,531 | 3 | Title: Utility Elicitation as a Classification Problem
Abstract: We investigate the application of classification techniques to utility elicitation. In a decision problem, two sets of parameters must generally be elicited: the probabilities and the utilities. While the prior and conditional probabilities in the model do not change from user to user, the utility models do. Thus it is necessary to elicit a utility model separately for each new user. Elicitation is long and tedious, particularly if the outcome space is large and not decomposable. There are two common approaches to utility function elicitation. The first is to base the determination of the user's utility function solely on elicitation of qualitative preferences. The second makes assumptions about the form and decomposability of the utility function. Here we take a different approach: we attempt to identify the new user's utility function based on classification relative to a database of previously collected utility functions. We do this by identifying clusters of utility functions that minimize an appropriate distance measure. Having identified the clusters, we develop a classification scheme that requires many fewer and simpler assessments than full utility elicitation and is more robust than utility elicitation based solely on preferences. We have tested our algorithm on a small database of utility functions in a prenatal diagnosis domain and the results are quite promising. | [
2566
] | Validation |
2,532 | 3 | Title: Ensemble Learning for Hidden Markov Models
Abstract: The standard method for training Hidden Markov Models optimizes a point estimate of the model parameters. This estimate, which can be viewed as the maximum of a posterior probability density over the model parameters, may be susceptible to over-fitting, and contains no indication of parameter uncertainty. Also, this maximum may be unrepresentative of the posterior probability distribution. In this paper we study a method in which we optimize an ensemble which approximates the entire posterior probability distribution. The ensemble learning algorithm requires the same resources as the traditional Baum-Welch algorithm. The traditional training algorithm for hidden Markov models is an expectation-maximization (EM) algorithm (Dempster et al. 1977) known as the Baum-Welch algorithm. It is a maximum likelihood method, or, with a simple modification, a penalized maximum likelihood method, which can be viewed as maximizing a posterior probability density over the model parameters. Recently, Hinton and van Camp (1993) developed a technique known as ensemble learning (see also MacKay (1995) for a review). Whereas maximum a posteriori methods optimize a point estimate of the parameters, in ensemble learning an ensemble is optimized, so that it approximates the entire posterior probability distribution over the parameters. The objective function that is optimized is a variational free energy (Feynman 1972) which measures the relative entropy between the approximating ensemble and the true distribution. In this paper we derive and test an ensemble learning algorithm for hidden Markov models, building on Neal | [
76,
518,
766,
2417
] | Validation |
2,533 | 2 | Title: An Object-Based Neural Model of Serial Processing in Visual Multielement Tracking
Abstract: A quantitative model is provided for psychophysical data on the tracking of multiple visual elements (multielement tracking). The model employs an object-based attentional mechanism for constructing and updating object representations. The model selectively enhances neural activations to serially construct and update the internal representations of objects through correlation-based changes in synaptic weights. The correspondence problem between items in memory and elements in the visual input is resolved through a combination of top-down prediction signals and bottom-up grouping processes. Simulations of the model on image sequences used in multielement tracking experiments show that reported results are consistent with a serial tracking mechanism that is based on psychophysical and neurobiological findings. In addition, simulations show that observed effects of perceptual grouping on tracking accuracy may result from the interactions between attention-guided predictions of object location and motion and grouping processes involved in solving the motion correspondence problem. | [
2517
] | Train |
2,534 | 5 | Title: Data Value Prediction Methods and Performance
Abstract: A quantitative model is provided for psychophysical data on the tracking of multiple visual elements (multielement tracking). The model employs an object-based attentional mechanism for constructing and updating object representations. The model selectively enhances neural activations to serially construct and update the internal representations of objects through correlation-based changes in synaptic weights. The correspondence problem between items in memory and elements in the visual input is resolved through a combination of top-down prediction signals and bottom-up grouping processes. Simulations of the model on image sequences used in multielement tracking experiments show that reported results are consistent with a serial tracking mechanism that is based on psychophysical and neurobiological findings. In addition, simulations show that observed effects of perceptual grouping on tracking accuracy may result from the interactions between attention-guided predictions of object location and motion and grouping processes involved in solving the motion correspondence problem. | [
2009
] | Train |
2,535 | 2 | Title: Adaptive Wavelet Control of Nonlinear Systems
Abstract: This paper considers the design and analysis of adaptive wavelet control algorithms for uncertain nonlinear dynamical systems. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control scheme based on nonlinearly parametrized wavelet network models. Semi-global stability results are obtained under the key assumption that the system uncertainty satisfies a "matching" condition. The localization properties of adaptive networks are discussed and formal definitions of interference and localization measures are proposed. | [
1668,
2176
] | Train |
2,536 | 4 | Title: Truncating Temporal Differences: On the Efficient Implementation of TD() for Reinforcement Learning
Abstract: Temporal difference (TD) methods constitute a class of methods for learning predictions in multi-step prediction problems, parameterized by a recency factor . Currently the most important application of these methods is to temporal credit assignment in reinforcement learning. Well known reinforcement learning algorithms, such as AHC or Q-learning, may be viewed as instances of TD learning. This paper examines the issues of the efficient and general implementation of TD() for arbitrary , for use with reinforcement learning algorithms optimizing the discounted sum of rewards. The traditional approach, based on eligibility traces, is argued to suffer from both inefficiency and lack of generality. The TTD (Truncated Temporal Differences) procedure is proposed as an alternative, that indeed only approximates TD(), but requires very little computation per action and can be used with arbitrary function representation methods. The idea from which it is derived is fairly simple and not new, but probably unexplored so far. Encouraging experimental results are presented, suggesting that using > 0 with the TTD procedure allows one to obtain a significant learning speedup at essentially the same cost as usual TD(0) learning. | [
502,
1957,
2629
] | Test |
2,537 | 2 | Title: Toward Learning Systems That Integrate Different Strategies and Representations TR93-22
Abstract: Temporal difference (TD) methods constitute a class of methods for learning predictions in multi-step prediction problems, parameterized by a recency factor . Currently the most important application of these methods is to temporal credit assignment in reinforcement learning. Well known reinforcement learning algorithms, such as AHC or Q-learning, may be viewed as instances of TD learning. This paper examines the issues of the efficient and general implementation of TD() for arbitrary , for use with reinforcement learning algorithms optimizing the discounted sum of rewards. The traditional approach, based on eligibility traces, is argued to suffer from both inefficiency and lack of generality. The TTD (Truncated Temporal Differences) procedure is proposed as an alternative, that indeed only approximates TD(), but requires very little computation per action and can be used with arbitrary function representation methods. The idea from which it is derived is fairly simple and not new, but probably unexplored so far. Encouraging experimental results are presented, suggesting that using > 0 with the TTD procedure allows one to obtain a significant learning speedup at essentially the same cost as usual TD(0) learning. | [
451,
1846,
1927,
2198
] | Train |
2,538 | 2 | Title: INCREMENTAL POLYNOMIAL CONTROLLER NETWORKS: two self-organising non-linear controllers
Abstract: Temporal difference (TD) methods constitute a class of methods for learning predictions in multi-step prediction problems, parameterized by a recency factor . Currently the most important application of these methods is to temporal credit assignment in reinforcement learning. Well known reinforcement learning algorithms, such as AHC or Q-learning, may be viewed as instances of TD learning. This paper examines the issues of the efficient and general implementation of TD() for arbitrary , for use with reinforcement learning algorithms optimizing the discounted sum of rewards. The traditional approach, based on eligibility traces, is argued to suffer from both inefficiency and lack of generality. The TTD (Truncated Temporal Differences) procedure is proposed as an alternative, that indeed only approximates TD(), but requires very little computation per action and can be used with arbitrary function representation methods. The idea from which it is derived is fairly simple and not new, but probably unexplored so far. Encouraging experimental results are presented, suggesting that using > 0 with the TTD procedure allows one to obtain a significant learning speedup at essentially the same cost as usual TD(0) learning. | [
2325
] | Train |
2,539 | 5 | Title: Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail
Abstract: This paper presents first results of an interdisciplinary project in scientific data mining. We analyze data about the carcinogenicity of chemicals derived from the carcinogenesis bioassay program performed by the US National Institute of Environmental Health Sciences. The database contains detailed descriptions of 6823 tests performed with more than 330 compounds and animals of different species, strains and sexes. The chemical structures are described at the atom and bond level, and in terms of various relevant structural properties. The goal of this paper is to investigate the effects that various levels of detail and amounts of information have on the resulting hypotheses, both quantitatively and qualitatively. We apply relational and propositional machine learning algorithms to learning problems formulated as regression or as classification tasks. In addition, these experiments have been conducted with two learning problems which are at different levels of detail. Quantitatively, our experiments indicate that additional information not necessarily improves accuracy. Qualitatively, a number of potential discoveries have been made by the algorithm for Relational Regression because it can utilize all the information contained in the relations of the database as well as in the numerical dependent variable. | [
1322,
1428,
2213
] | Train |
2,540 | 2 | Title: Efficient Implementation of Gaussian Processes
Abstract: Neural networks and Bayesian inference provide a useful framework within which to solve regression problems. However their parameterization means that the Bayesian analysis of neural networks can be difficult. In this paper, we investigate a method for regression using Gaussian process priors which allows exact Bayesian analysis using matrix manipulations. We discuss the workings of the method in detail. We will also detail a range of mathematical and numerical techniques that are useful in applying Gaussian processes to general problems including efficient approximate matrix inversion methods developed by Skilling. | [
157,
160,
611,
1857
] | Train |
2,541 | 1 | Title: PLEASE: A prototype learning system using genetic algorithms
Abstract: Prototypes have been proposed as representation of concepts that are used effectively by humans. Developing computational schemes for generating prototypes from examples, however, has proved to be a difficult problem. We present a novel genetic algorithm based prototype learning system, PLEASE, for constructing appropriate prototypes from classified training instances. After constructing a set of prototypes for each of the possible classes, the class of a new input instance is determined by the nearest prototype to this instance. Attributes are assumed to be ordinal in nature and prototypes are represented as sets of feature-value pairs. A genetic algorithm is used to evolve the number of prototypes per class and their positions on the input space. We present experimental results on a series of artificial problems of varying complexity. PLEASE performs competitively with several nearest neighbor classification algorithms on the problem set. An analysis of the strengths and weaknesses of the initial version of our system motivates the need for additional operators. The inclusion of these operators substantially improves the performance of the system on particularly difficult problems. | [
638,
686,
2673
] | Validation |
2,542 | 2 | Title: Worst-Case Identification of Nonlinear Fading Memory Systems
Abstract: In this paper, the problem of asymptotic identification for a class of fading memory systems in the presence of bounded noise is studied. For any experiment, the worst-case error is characterized in terms of the diameter of the worst-case uncertainty set. Optimal inputs that minimize the radius of uncertainty are studied and characterized. Finally, a convergent algorithm that does not require knowledge of the noise upper bound is furnished. The algorithm is based on interpolating data with spline functions, which are shown to be well suited for identification in the presence of bounded noise; more so than other basis functions such as polynomials. The methods as well as the results are quite general and are applicable to a larger variety of settings. | [
2236,
2262
] | Validation |
2,543 | 3 | Title: Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases
Abstract: This paper describes Rapture | a system for revising probabilistic knowledge bases that combines connectionist and symbolic learning methods. Rapture uses a modified version of backpropagation to refine the certainty factors of a probabilistic rule base and it uses ID3's information-gain heuristic to add new rules. Results on refining three actual expert knowledge bases demonstrate that this combined approach generally performs better than previous methods. | [
136,
159,
1479,
1776,
2409,
2440,
2674
] | Validation |
2,544 | 0 | Title: Choosing Learning Strategies to Achieve Learning Goals
Abstract: In open world applications a number of machine-learning techniques may potentially apply to a given learning situation. The research presented here illustrates the complexity involved in automatically choosing an appropriate technique in a multistrategy learning system. It also constitutes a step toward a general computational solution to the learning-strategy selection problem. The approach is to treat learning-strategy selection as a separate planning problem with its own set of goals, as is the case with ordinary problem-solvers. Therefore, the management and pursuit of these learning goals becomes a central issue in learning, similar to the goal-management problems associated with traditional planning systems. This paper explores some issues, problems, and possible solutions in such a framework. Examples are presented from a multistrategy learning system called Meta-AQUA. | [
2568
] | Validation |
2,545 | 2 | Title: Volatility of Volatility of Financial Markets
Abstract: We present empirical evidence for considering volatility of Eurodollar futures as a stochastic process, requiring a generalization of the standard Black-Scholes (BS) model which treats volatility as a constant. We use a previous development of a statistical mechanics of financial markets (SMFM) to model these issues. | [
1773,
1794,
1795,
2082,
2178
] | Train |
2,546 | 3 | Title: Plausibility Measures: A User's Guide
Abstract: We examine a new approach to modeling uncertainty based on plausibility measures, where a plausibility measure just associates with an event its plausibility, an element is some partially ordered set. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probability measures, belief functions, and possibility measures. The lack of structure in a plausibility measure makes it easy for us to add structure on an as needed basis, letting us examine what is required to ensure that a plausibility measure has certain properties of interest. This gives us insight into the essential features of the properties in question, while allowing us to prove general results that apply to many approaches to reasoning about uncertainty. Plausibility measures have already proved useful in analyzing default reasoning. In this paper, we examine their algebraic properties, analogues to the use of + and fi in probability theory. An understanding of such properties will be essential if plausibility measures are to be used in practice as a representation tool. | [
276,
342,
1993
] | Train |
2,547 | 0 | Title: Temporal abstractions for pre-processing and interpreting diabetes monitoring time series
Abstract: In this paper we describe a number of intelligent data analysis techniques to pre-process and analyze data coming from home monitoring of diabetic patients. In particular, we show how the combination of temporal abstractions with statistical and probabilistic techniques may be applied to derive useful summaries of patients' behaviour over a certain monitoring period. Finally, we describe how Intelligent Data Analysis methods may be used to index past cases to perform a case-based re trieval in a data-base of past cases. | [
2492
] | Test |
2,548 | 6 | Title: A Framework for Multiple-Instance Learning
Abstract: Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled negative only if all the instances in it are negative. We describe a new general framework, called Diverse Density, for solving multiple-instance learning problems. We apply this framework to learn a simple description of a person from a series of images (bags) containing that person, to a stock selection problem, and to the drug activity prediction problem. | [
507,
2391,
2427
] | Validation |
2,549 | 2 | Title: A Generalized Approximate Cross Validation for Smoothing Splines with Non-Gaussian Data 1
Abstract: Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled negative only if all the instances in it are negative. We describe a new general framework, called Diverse Density, for solving multiple-instance learning problems. We apply this framework to learn a simple description of a person from a series of images (bags) containing that person, to a stock selection problem, and to the drug activity prediction problem. | [
193,
280,
519,
2608
] | Train |
2,550 | 5 | Title: Language Series Revisited: The Complexity of Hypothesis Spaces in ILP
Abstract: Restrictions on the number and depth of existential variables as defined in the language series of Clint [Rae92] are widely used in ILP and expected to produce a considerable reduction in the size of the hypothesis space. In this paper we show that this is generally not the case. The lower bounds we present lead to intractable hypothesis spaces except for toy domains. We argue that the parameters chosen in Clint are unsuitable for sensible bias shift operations, and propose alternative approaches resulting in the desired reduction of the hypothesis space and allowing for a natural integration of the shift of bias. | [
2045
] | Train |
2,551 | 4 | Title: The Role of Forgetting in Learning
Abstract: This paper is a discussion of the relationship between learning and forgetting. An analysis of the economics of learning is carried out and it is argued that knowledge can sometimes have a negative value. A series of experiments involving a program which learns to traverse state spaces is described. It is shown that most of the knowledge acquired is of negative value even though it is correct and was acquired solving similar problems. It is shown that the value of the knowledge depends on what else is known and that random forgetting can sometimes lead to substantial improvements in performance. It is concluded that research into knowledge acquisition should take seriously the possibility that knowledge may sometimes be harmful. The view is taken that learning and forgetting are complementary processes which construct and maintain useful representations of experience. | [
523,
2473
] | Train |
2,552 | 2 | Title: Inferring sparse, overcomplete image codes using an efficient coding framework
Abstract: We apply a general technique for learning overcomplete bases to the problem of finding efficient image codes. The bases learned by the algorithm are localized, oriented, and bandpass, consistent with earlier results obtained using related methods. We show that the learned bases are Gabor-like in structure and that higher degrees of overcompleteness produce greater sampling density in position, orientation, and scale. The efficient coding framework provides a method for comparing different bases objectively by calculating their probability given the observed data or by measuring the entropy of the basis function coefficients. Compared to complete and overcomplete Fourier and wavelet bases, the learned bases have much better coding efficiency. We demonstrate the improvement in the representation of the learned bases by showing superior performance in image denoising and filling-in of missing pixels. | [
576,
2026
] | Validation |
2,553 | 2 | Title: TURING COMPUTABILITY WITH NEURAL NETS
Abstract: This paper shows the existence of a finite neural network, made up of sigmoidal neurons, which simulates a universal Turing machine. It is composed of less than 10 5 synchronously evolving processors, interconnected linearly. High-order connections are not required. | [
1875
] | Train |
2,554 | 1 | Title: Genetic Programming Estimates of Kolmogorov Complexity
Abstract: In this paper the problem of the Kolmogorov complexity related to binary strings is faced. We propose a Genetic Programming approach which consists in evolving a population of Lisp programs looking for the optimal program that generates a given string. This evolutionary approach has permited to overcome the intractable space and time difficulties occurring in methods which perform an approximation of the Kolmogorov complexity function. The experimental results are quite significant and also show interesting computational strategies so proving the effectiveness of the implemented technique. | [
163,
1850
] | Train |
2,555 | 6 | Title: Knowing What Doesn't Matter: Exploiting Omitted Superfluous Data
Abstract: Most inductive inference algorithms (i.e., "learners") work most effectively when their training data contain completely specified labeled samples. In many diagnostic tasks, however, the data will include the values of only some of the attributes; we model this as a blocking process that hides the values of those attributes from the learner. While blockers that remove the values of critical attributes can handicap a learner, this paper instead focuses on blockers that remove only superfluous attribute values, i.e., values that are not needed to classify an instance, given the values of the other unblocked attributes. We first motivate and formalize this model of "superfluous-value blocking," and then demonstrate that these omissions can be useful, by showing that certain classes that seem hard to learn in the general PAC model | viz., decision trees | are trivial to learn in this setting, and can even be learned in a manner that is very robust to classification noise. We also discuss how this model can be extended to deal with (1) theory revision (i.e., modifying an existing decision tree); (2) "complex" attributes (which correspond to combinations of other atomic attributes); (3) blockers that occasionally include superfluous values or exclude re quired values; and (4) other hypothesis classes (e.g., DNF formulae). Declaration: This paper has not already been accepted by and is not currently under review for a journal or another conference, nor will it be submitted for such during IJCAI's review period. fl This is an extended version of a paper that appeared in working notes of the 1994 AAAI Fall Symposium on "Relevance", New Orleans, November 1994. y Authors listed alphabetically. We gratefully acknowledge receiving helpful comments from Dale Schuurmans and George Drastal. | [
2560
] | Train |
2,556 | 0 | Title: A Case-Based Approach to Reactive Control for Autonomous Robots
Abstract: We propose a case-based method of selecting behavior sets as an addition to traditional reactive robotic control systems. The new system (ACBARR | A Case BAsed Reactive Robotic system) provides more flexible performance in novel environments, as well as overcoming a standard "hard" problem for reactive systems, the box canyon. Additionally, ACBARR is designed in a manner which is intended to remain as close to pure reactive control as possible. Higher level reasoning and memory functions are intentionally kept to a minimum. As a result, the new reasoning does not significantly slow the system down from pure reactive speeds. | [
858,
1904
] | Train |
2,557 | 2 | Title: Growing Simpler Decision Trees to Facilitate Knowledge Discovery
Abstract: When using machine learning techniques for knowledge discovery, output that is comprehensible to a human is as important as predictive accuracy. We introduce a new algorithm, SET-Gen, that improves the comprehensibility of decision trees grown by standard C4.5 without reducing accuracy. It does this by using genetic search to select the set of input features C4.5 is allowed to use to build its tree. We test SET-Gen on a wide variety of real-world datasets and show that SET-Gen trees are significantly smaller and reference significantly fewer features than trees grown by C4.5 without using SET-Gen. Statistical significance tests show that the accuracies of SET-Gen's trees are either not distinguishable from or are more accurate than those of the original C4.5 trees on all ten datasets tested. | [
163,
430,
686,
1947
] | Validation |
2,558 | 3 | Title: Using Bayesian networks for incorporating probabilistic a priori knowledge into Boltzmann machines
Abstract: We present a method for automatically determining the structure and the connection weights of a Boltzmann machine corresponding to a given Bayesian network representation of a probability distribution on a set of discrete variables. The resulting Boltzmann machine structure can be implemented efficiently on massively parallel hardware, since the structure can be divided into two separate clusters where all the nodes in one cluster can be updated simultaneously. The updating process of the Boltzmann machine approximates a Gibbs sampling process of the original Bayesian network in the sense that the Boltzmann machine converges to the same final state as the Gibbs sampler does. The mapping from a Bayesian network to a Boltzmann machine can be seen as a method for incorporating probabilistic a priori information into a neural network architecture, which can then be trained further with existing learning algorithms. | [
450,
2514
] | Test |
2,559 | 3 | Title: "Linear Dependencies Represented by Chain Graphs," "Graphical Modelling With MIM," Manual. "Identifying Independence in Bayesian
Abstract: 8] Dori, D. and Tarsi, M., "A Simple Algorithm to Construct a Consistent Extension of a Partially Oriented Graph," Computer Science Department, Tel-Aviv University. Also Technical Report R-185, UCLA, Cognitive Systems Laboratory, October 1992. [14] Pearl, J. and Wermuth, N., "When Can Association Graphs Admit a Causal Interpretation?," UCLA, Cognitive Systems Laboratory, Technical Report R-183-L, November 1992. [17] Verma, T.S. and Pearl, J., "Deciding Morality of Graphs is NP-complete," Technical Report R-188, UCLA, Cognitive Systems Laboratory, October 1992. | [
51,
260,
841,
1241,
1747,
2076
] | Train |
2,560 | 6 | Title: Learning Default Concepts
Abstract: Classical concepts, based on necessary and sufficient defining conditions, cannot classify logically insufficient object descriptions. Many reasoning systems avoid this limitation by using "default concepts" to classify incompletely described objects. We address the task of learning such default concepts from observational data. We model the underlying performance task | classifying incomplete examples | by a probabilistic process where random test examples are passed through a "blocker" that can hide object attributes from the classifier. We then address the task of learning accurate default concepts from random training examples. We survey the learning techniques that have been proposed for this task in the machine learning and knowledge representation literatures, investigate the relative merits of each, and show that a superior learning technique can be developed from well known statistical principles. Finally, we extend Valiant's pac-learning framework to this context and obtain a number of useful learnability results. | [
251,
323,
649,
865,
1505,
2555
] | Train |
2,561 | 3 | Title: MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains
Abstract: Given a problem, a case-based reasoning (CBR) system will search its case memory and use the stored cases to find the solution, possibly modifying retrieved cases to adapt to the required input specifications. In discrete domains CBR reasoning can be based on a rigorous Bayesian probability propagation algorithm. Such a Bayesian CBR system can be implemented as a probabilistic feedforward neural network with one of the layers representing the cases. In this paper we introduce a Minimum Description Length (MDL) based learning algorithm to obtain the proper network structure with the associated conditional probabilities. This algorithm together with the resulting neural network implementation provide a massively parallel architecture for solving the efficiency bottleneck in case-based reasoning. | [
485,
719,
1527,
1908,
2380
] | Train |
2,562 | 2 | Title: NONLINEAR TRADING MODELS THROUGH SHARPE RATIO MAXIMIZATION
Abstract: Working Paper IS-97-005, Leonard N. Stern School of Business, New York University. In: Decision Technologies for Financial Engineering (Proceedings of the Fourth International Conference on Neural Networks in the Capital Markets, NNCM-96), pp. 3-22. Edited by A.S.Weigend, Y.S.Abu-Mostafa, and A.-P.N.Refenes. Singapore: World Scientific, 1997. http://www.stern.nyu.edu/~aweigend/Research/Papers/SharpeRatio While many trading strategies are based on price prediction, traders in financial markets are typically interested in risk-adjusted performance such as the Sharpe Ratio, rather than price predictions themselves. This paper introduces an approach which generates a nonlinear strategy that explicitly maximizes the Sharpe Ratio. It is expressed as a neural network model whose output is the position size between a risky and a risk-free asset. The iterative parameter update rules are derived and compared to alternative approaches. The resulting trading strategy is evaluated and analyzed on both computer-generated data and real world data (DAX, the daily German equity index). Trading based on Sharpe Ratio maximization compares favorably to both profit optimization and probability matching (through cross-entropy optimization). The results show that the goal of optimizing out-of-sample risk-adjusted profit can be achieved with this nonlinear approach. | [
668,
1366,
2595
] | Train |
2,563 | 1 | Title: Analysis of Neurocontrollers Designed by Simulated Evolution
Abstract: Randomized, adaptive, greedy search using evolutionary algorithms offers a powerful and versatile approach to the automated design of neural network architectures for a variety of tasks in artificial intelligence and robotics. In this paper we present results from the evolutionary design of a neuro-controller for a robotic bulldozer. This robot is given the task of clearing an arena littered with boxes by pushing boxes to the sides. Through a careful analysis of the evolved networks we show how evolution exploits the design constraints and properties of the environment to produce network structures of high fitness. We conclude with a brief summary of related ongoing research examining the intricate interplay between environment and evolutionary processes in determining the structure and function of the resulting neural architectures. | [
163,
219,
1583,
2220,
2396
] | Test |
2,564 | 1 | Title: Embedding of a sequential procedure within an evolutionary algorithm for coloring problems in graphs
Abstract: Randomized, adaptive, greedy search using evolutionary algorithms offers a powerful and versatile approach to the automated design of neural network architectures for a variety of tasks in artificial intelligence and robotics. In this paper we present results from the evolutionary design of a neuro-controller for a robotic bulldozer. This robot is given the task of clearing an arena littered with boxes by pushing boxes to the sides. Through a careful analysis of the evolved networks we show how evolution exploits the design constraints and properties of the environment to produce network structures of high fitness. We conclude with a brief summary of related ongoing research examining the intricate interplay between environment and evolutionary processes in determining the structure and function of the resulting neural architectures. | [
163,
1159,
1558,
1785
] | Validation |
2,565 | 0 | Title: Defining and Combining Symmetric and Asymmetric Similarity Measures
Abstract: In this paper, we present a framework for the definition of similarity measures using lattice-valued functions. We show their strengths (particularly for combining similarity measures). Then we investigate a particular instantiation of the framework, in which sets are used both to represent objects and to denote degrees of similarity. The paper con cludes by suggesting some generalisations of the findings. | [
2157
] | Validation |
2,566 | 3 | Title: A Constraint-Based Approach to Preference Elicitation and Decision Making
Abstract: We investigate the solution of constraint-based configuration problems in which the preference function over outcomes is unknown or incompletely specified. The aim is to configure a system, such as a personal computer, so that it will be optimal for a given user. The goal of this project is to develop algorithms that generate the most preferred feasible configuration by posing preference queries to the user. In order to minimize the number and the complexity of preference queries posed to the user, the algorithm reasons about the user's preferences while taking into account constraints over the set of feasible configurations. We assume that the user can structure their preferences in a particular way that, while natural in many settings, can be exploited during the optimization process. We also address in a preliminary fashion the trade-offs between computational effort in the solution of a problem and the degree of interaction with the user. | [
62,
2531
] | Train |
2,567 | 2 | Title: On the Combination of Supervised and Unsupervised Learning reducing the overall error measure of a classifier.
Abstract: | [
359,
2498
] | Validation |
2,568 | 0 | Title: Abstract
Abstract: Self-selection of input examples on the basis of performance failure is a powerful bias for learning systems. The definition of what constitutes a learning bias, however, has been typically restricted to bias provided by the input language, hypothesis language, and preference criteria between competing concept hypotheses. But if bias is taken in the broader context as any basis that provides a preference for one concept change over another, then the paradigm of failure-driven processing indeed provides a bias. Bias is exhibited by the selection of examples from an input stream that are examples of failure; successful performance is filtered out. We show that the degrees of freedom are less in failure-driven learning than in success-driven learning and that learning is facilitated because of this constraint. We also broaden the definition of failure, provide a novel taxonomy of failure causes, and illustrate the interaction of both in a multistrategy learning system called Meta-AQUA. | [
289,
583,
612,
717,
2544
] | Train |
2,569 | 2 | Title: The Gamma MLP Using Multiple Temporal Resolutions for Improved Classification
Abstract: We have previously introduced the Gamma MLP which is defined as an MLP with the usual synaptic weights replaced by gamma filters and associated gain terms throughout all layers. In this paper we apply the Gamma MLP to a larger scale speech phoneme recognition problem, analyze the operation of the network, and investigate why the Gamma MLP can perform better than alternatives. The Gamma MLP is capable of employing multiple temporal resolutions (the temporal resolution is defined here, as per de Vries and Principe, as the number of parameters of freedom (i.e. the number of tap variables) per unit of time in the gamma memory this is equal to the gamma memory parameter as detailed in the paper). Multiple temporal resolutions may be advantageous for certain problems, e.g. different resolutions may be optimal for extracting different features from the input data. For the problem in this paper, the Gamma MLP is observed to use a large range of temporal resolutions. In comparison, TDNN networks typically use only a single temporal resolution. Further motivation for the Gamma MLP is related to the curse of dimensionality and the ability of the Gamma MLP to trade off temporal resolution for memory depth, and therefore increase memory depth without increasing the dimensionality of the network. The IIR MLP is a more general version of the Gamma MLP however the IIR MLP performs poorly for the problem in this paper. Investigation suggests that the error surface of the Gamma MLP is more suitable for gradient descent training than the error surface of the IIR MLP. | [
1820
] | Train |
2,570 | 2 | Title: In Fast Non-Linear Dimension Reduction
Abstract: We present a fast algorithm for non-linear dimension reduction. The algorithm builds a local linear model of the data by merging PCA with clustering based on a new distortion measure. Experiments with speech and image data indicate that the local linear algorithm produces encodings with lower distortion than those built by five layer auto-associative networks. The local linear algorithm is also more than an order of magnitude faster to train. | [
480,
667,
1806,
1928
] | Validation |
2,571 | 2 | Title: Non-Deterministic, Constraint-Based Parsing of Human Genes
Abstract: We present a fast algorithm for non-linear dimension reduction. The algorithm builds a local linear model of the data by merging PCA with clustering based on a new distortion measure. Experiments with speech and image data indicate that the local linear algorithm produces encodings with lower distortion than those built by five layer auto-associative networks. The local linear algorithm is also more than an order of magnitude faster to train. | [
268,
613,
1878,
2107,
2496
] | Train |
2,572 | 2 | Title: Negative observations concerning approximations from spaces generated by scattered shifts of functions vanishing at 1
Abstract: Approximation by scattered shifts f( ff)g ff2A of a basis function are considered, and different methods for localizing these translates are compared. It is argued in the note that the superior localization processes are those that employ the original translates only. | [
365,
2112
] | Train |
2,573 | 3 | Title: An Optimum Decision Rule for Pattern Recognition
Abstract: | [
1942
] | Train |
2,574 | 2 | Title: Identification of Protein Coding Regions In Genomic DNA Molecular, Cellular and Developmental Biology, Keywords: gene
Abstract: | [
427,
2107
] | Validation |
2,575 | 3 | Title: The Stationary Wavelet Transform and some Statistical Applications
Abstract: Wavelets are of wide potential use in statistical contexts. The basics of the discrete wavelet transform are reviewed using a filter notation that is useful subsequently in the paper. A `stationary wavelet transform', where the coefficient sequences are not decimated at each stage, is described. Two different approaches to the construction of an inverse of the stationary wavelet transform are set out. The application of the stationary wavelet transform as an exploratory statistical method is discussed, together with its potential use in nonparametric regression. A method of local spectral density estimation is developed. This involves extensions to the wavelet context of standard time series ideas such as the periodogram and spectrum. The technique is illustrated by its application to data sets from astronomy and veterinary anatomy. | [
1910,
2366,
2388,
2506
] | Validation |
2,576 | 2 | Title: A Neural Model of the Cortical Representation of Egocentric Distance
Abstract: Wavelets are of wide potential use in statistical contexts. The basics of the discrete wavelet transform are reviewed using a filter notation that is useful subsequently in the paper. A `stationary wavelet transform', where the coefficient sequences are not decimated at each stage, is described. Two different approaches to the construction of an inverse of the stationary wavelet transform are set out. The application of the stationary wavelet transform as an exploratory statistical method is discussed, together with its potential use in nonparametric regression. A method of local spectral density estimation is developed. This involves extensions to the wavelet context of standard time series ideas such as the periodogram and spectrum. The technique is illustrated by its application to data sets from astronomy and veterinary anatomy. | [
1051,
2678
] | Train |
2,577 | 3 | Title: Targeting Business Users with Decision Table Classifiers
Abstract: Business users and analysts commonly use spreadsheets and 2D plots to analyze and understand their data. On-line Analytical Processing (OLAP) provides these users with added flexibility in pivoting data around different attributes and drilling up and down the multi-dimensional cube of aggregations. Machine learning researchers, however, have concentrated on hypothesis spaces that are foreign to most users: hyper-planes (Perceptrons), neural networks, Bayesian networks, decision trees, nearest neighbors, etc. In this paper we advocate the use of decision table classifiers that are easy for line-of-business users to understand. We describe several variants of algorithms for learning decision tables, compare their performance, and describe a visualization mechanism that we have implemented in MineSet. The performance of decision tables is comparable to other known algorithms, such as C4.5/C5.0, yet the resulting classifiers use fewer attributes and are more comprehensible. | [
1020,
2180,
2342,
2367,
2508
] | Test |
2,578 | 3 | Title: Analysis of hospital quality monitors using hierarchical time series models
Abstract: The VA management services department invests considerably in the collection and assessment of data to inform on hospital and care-area specific levels of quality of care. Resulting time series of quality monitors provide information relevant to evaluating patterns of variability in hospital-specific quality of care over time and across care areas, and to compare and assess differences across hospitals. In collaboration with the VA management services group we have developed various models for evaluating such patterns of dependencies and combining data across the VA hospital system. This paper provides a brief overview of resulting models, some summary examples on three monitor time series, and discussion of data, modelling and inference issues. This work introduces new models for multivariate non-Gaussian time series. The framework combines cross-sectional, hierarchical models of the population of hospitals with time series structure to allow and measure time-variations in the associated hierarchical model parameters. In the VA study, the within-year components of the models describe patterns of heterogeneity across the population of hospitals and relationships among several such monitors, while the time series components describe patterns of variability through time in hospital-specific effects and their relationships across quality monitors. Additional model components isolate unpredictable aspects of variability in quality monitor outcomes, by hospital and care areas. We discuss model assessment, residual analysis and MCMC algorithms developed to fit these models, which will be of interest in related applications in other socio-economic areas. | [
99,
2679
] | Train |
2,579 | 2 | Title: SPERT-II: A Vector Microprocessor System and its Application to Large Problems in Backpropagation Training
Abstract: We report on our development of a high-performance system for neural network and other signal processing applications. We have designed and implemented a vector microprocessor and packaged it as an attached processor for a conventional workstation. We present performance comparisons with commercial workstations on neural network backpropagation training. The SPERT-II system demonstrates significant speedups over extensively hand optimization code running on the workstations. | [
2279,
2336
] | Train |
2,580 | 6 | Title: The Challenge of Revising an Impure Theory
Abstract: A pure rule-based program will return a set of answers to each query; and will return the same answer set even if its rules are re-ordered. However, an impure program, which includes the Prolog cut "!" and not() operators, can return different answers if the rules are re-ordered. There are also many reasoning systems that return only the first answer found for each query; these first answers, too, depend on the rule order, even in pure rule-based systems. A theory revision algorithm, seeking a revised rule-base whose expected accuracy, over the distribution of queries, is optimal, should therefore consider modifying the order of the rules. This paper first shows that a polynomial number of training "labeled queries" (each a query coupled with its correct answer) provides the distribution information necessary to identify the optimal ordering. It then proves, however, that the task of determining which ordering is optimal, once given this information, is intractable even in trivial situations; e.g., even if each query is an atomic literal, we are seeking only a "perfect" theory, and the rule base is propositional. We also prove that this task is not even approximable: Unless P = N P , no polynomial time algorithm can produce an ordering of an n-rule theory whose accuracy is within n fl of optimal, for some fl > 0. We also prove similar hardness, and non-approximatability, results for the related tasks of determining, in these impure contexts, (1) the optimal ordering of the antecedents; (2) the optimal set of rules to add or (3) to delete; and (4) the optimal priority values for a set of defaults. | [
52,
136,
1819,
1823
] | Train |
2,581 | 0 | Title: Four Challenges for a Computational Model of Legal Precedent
Abstract: Identifying the open research issues in a field is a necessary step for progress in that field. This paper describes four open research problems in computational models of precedent-based legal reasoning: relating case representation to precedent use; modeling the selection and construction of both arguments based on pairwise case comparison and multiple-precedent arguments; modeling the process whereby purposes, policies, and principles are used in case similarity assessment; and extending the applicability of precedents to tasks other than classification. | [
649,
2403
] | Validation |
2,582 | 2 | Title: Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference
Abstract: Financial forecasting is an example of a signal processing problem which is challenging due to small sample sizes, high noise, non-stationarity, and non-linearity. Neural networks have been very successful in a number of signal processing applications. We discuss fundamental limitations and inherent difficulties when using neural networks for the processing of high noise, small sample size signals. We introduce a new intelligent signal processing method which addresses the difficulties. The method uses conversion into a symbolic representation with a self-organizing map, and grammatical inference with recurrent neural networks. We apply the method to the prediction of daily foreign exchange rates, addressing difficulties with non-stationarity, overfitting, and unequal a priori class probabilities, and we find significant predictability in comprehensive experiments covering 5 different foreign exchange rates. The method correctly predicts the direction of change for the next day with an error rate of 47.1%. The error rate reduces to around 40% when rejecting examples where the system has low confidence in its prediction. The symbolic representation aids the extraction of symbolic knowledge from the recurrent neural networks in the form of deterministic finite state automata. These automata explain the operation of the system and are often relatively simple. Rules related to well known behavior such as trend following and mean reversal are extracted. | [
409,
411,
462,
1718,
2178
] | Train |
2,583 | 6 | Title: Dynamic Automatic Model Selection
Abstract: COINS Technical Report 92-30 February 1992 Abstract The problem of how to learn from examples has been studied throughout the history of machine learning, and many successful learning algorithms have been developed. A problem that has received less attention is how to select which algorithm to use for a given learning task. The ability of a chosen algorithm to induce a good generalization depends on how appropriate the model class underlying the algorithm is for the given task. We define an algorithm's model class to be the representation language it uses to express a generalization of the examples. Supervised learning algorithms differ in their underlying model class and in how they search for a good generalization. Given this characterization, it is not surprising that some algorithms find better generalizations for some, but not all tasks. Therefore, in order to find the best generalization for each task, an automated learning system must search for the appropriate model class in addition to searching for the best generalization within the chosen class. This thesis proposal investigates the issues involved in automating the selection of the appropriate model class. The presented approach has two facets. Firstly, the approach combines different model classes in the form of a model combination decision tree, which allows the best representation to be found for each subconcept of the learning task. Secondly, which model class is the most appropriate is determined dynamically using a set of heuristic rules. Explicit in each rule are the conditions in which a particular model class is appropriate and if it is not, what should be done next. In addition to describing the approach, this proposal describes how the approach will be evaluated in order to demonstrate that it is both an efficient and effective method for automatic model selection. | [
102,
378,
1173,
1423,
2135,
2310,
2333
] | Train |
2,584 | 2 | Title: Presynaptic and Postsynaptic Competition in Models for the Development of Neuromuscular Connections
Abstract: The development of the nervous system involves in many cases interactions on a local scale rather than the execution of a fully specified genetic blueprint. The problem is to discover the nature of these interactions and the factors on which they depend. The withdrawal of polyinnervation in developing muscle is an example where such competitive interactions play an important role. We examine the possible types of competition in formal | [
2632
] | Test |
2,585 | 0 | Title: Rule Induction and Instance-Based Learning: A Unified Approach
Abstract: This paper presents a new approach to inductive learning that combines aspects of instance-based learning and rule induction in a single simple algorithm. The RISE system searches for rules in a specific-to-general fashion, starting with one rule per training example, and avoids some of the difficulties of separate-and-conquer approaches by evaluating each proposed induction step globally, i.e., through an efficient procedure that is equivalent to checking the accuracy of the rule set as a whole on every training example. Classification is performed using a best-match strategy, and reduces to nearest-neighbor if all generalizations of instances were rejected. An extensive empirical study shows that RISE consistently achieves higher accuracies than state-of-the-art representatives of its "parent" paradigms (PEBLS and CN2), and also outperforms a decision-tree learner (C4.5) in 13 out of 15 test domains (in | [
1263,
1809,
1830,
2441
] | Train |
2,586 | 4 | Title: Learning One More Thing
Abstract: Most research on machine learning has focused on scenarios in which a learner faces a single, isolated learning task. The lifelong learning framework assumes that the learner encounters a multitude of related learning tasks over its lifetime, providing the opportunity for the transfer of knowledge among these. This paper studies lifelong learning in the context of binary classification. It presents the invariance approach, in which knowledge is transferred via a learned model of the invariances of the domain. Results on learning to recognize objects from color images demonstrate superior generalization capabilities if invariances are learned and used to bias subsequent learning. | [
1647,
1889,
2113,
2162,
2486
] | Test |
2,587 | 5 | Title: Predicate Invention and Learning from Positive Examples Only
Abstract: Previous bias shift approaches to predicate invention are not applicable to learning from positive examples only, if a complete hypothesis can be found in the given language, as negative examples are required to determine whether new predicates should be invented or not. One approach to this problem is presented, MERLIN 2.0, which is a successor of a system in which predicate invention is guided by sequences of input clauses in SLD-refutations of positive and negative examples w.r.t. an overly general theory. In contrast to its predecessor which searches for the minimal finite-state automaton that can generate all positive and no negative sequences, MERLIN 2.0 uses a technique for inducing Hidden Markov Models from positive sequences only. This enables the system to invent new predicates without being triggered by negative examples. Another advantage of using this induction technique is that it allows for incremental learning. Experimental results are presented comparing MERLIN 2.0 with the positive only learning framework of Progol 4.2 and comparing the original induction technique with a new version that produces deterministic Hidden Markov Models. The results show that predicate invention may indeed be both necessary and possible when learning from positive examples only as well as it can be beneficial to keep the induced model deterministic. | [
2312
] | Train |
2,588 | 3 | Title: Some recent ideas on utility (and probability) (not for distribution or reference)
Abstract: | [
2301
] | Train |
2,589 | 5 | Title: Pac-Learning Recursive Logic Programs: Efficient Algorithms
Abstract: We present algorithms that learn certain classes of function-free recursive logic programs in polynomial time from equivalence queries. In particular, we show that a single k-ary recursive constant-depth determinate clause is learnable. Two-clause programs consisting of one learnable recursive clause and one constant-depth determinate non-recursive clause are also learnable, if an additional "basecase" oracle is assumed. These results immediately imply the pac-learnability of these classes. Although these classes of learnable recursive programs are very constrained, it is shown in a companion paper that they are maximally general, in that generalizing either class in any natural way leads to a compu-tationally difficult learning problem. Thus, taken together with its companion paper, this paper establishes a boundary of efficient learnability for recursive logic programs. | [
344,
2329,
2424
] | Train |
2,590 | 3 | Title: Backfitting in Smoothing Spline ANOVA
Abstract: A scheme to compute smoothing spline ANOVA estimates for large data sets with a (near) tensor-product structure is proposed. Such data sets are common in spatial-temporal analysis and image analysis. This scheme combines backfitting algorithm with iterative imputation algorithm in order to save both computational space and time. The convergence of this algorithm and various ways to further speed it up, such as collapsing component functions and successive over-relaxation, are discussed. Issues related to its application in spatial-temporal analysis are discussed too. An application to a global analysis of historical surface temperature data is described. | [
388,
420,
519,
2421
] | Test |
2,591 | 5 | Title: Lookahead and Discretization in ILP
Abstract: We present and evaluate two methods for improving the performance of ILP systems. One of them is discretization of numerical attributes, based on Fayyad and Irani's text [9], but adapted and extended in such a way that it can cope with some aspects of discretization that only occur in relational learning problems (when indeterminate literals occur). The second technique is lookahead. It is a well-known problem in ILP that a learner cannot always assess the quality of a refinement without knowing which refinements will be enabled afterwards, i.e. without looking ahead in the refinement lattice. We present a simple method for specifying when lookahead is to be used, and what kind of lookahead is interesting. Both the discretization and lookahead techniques are evaluated experimentally. The results show that both techniques improve the quality of the induced theory, while computational costs are acceptable. | [
2126,
2253,
2427,
2431
] | Test |
2,592 | 3 | Title: FILTERING VIA SIMULATION: AUXILIARY PARTICLE FILTERS
Abstract: This paper analyses the recently suggested particle approach to filtering time series. We suggest that the algorithm is not robust to outliers for two reasons: the design of the simulators and the use of the discrete support to represent the sequentially updating prior distribution. Both problems are tackled in this paper. We believe we have largely solved the first problem and have reduced the order of magnitude of the second. In addition we introduce the idea of stratification into the particle filter which allows us to perform on-line Bayesian calculations about the parameters which index the models and maximum likelihood estimation. The new methods are illustrated by using a stochastic volatility model and a time series model of angles. | [
99,
1852
] | Test |
2,593 | 0 | Title: Induction of Condensed Determinations
Abstract: In this paper we suggest determinations as a representation of knowledge that should be easy to understand. We briefly review determinations, which can be displayed in a tabular format, and their use in prediction, which involves a simple matching process. We describe ConDet, an algorithm that uses feature selection to construct determinations from training data, augmented by a condensation process that collapses rows to produce simpler structures. We report experiments that show condensation reduces complexity with no loss of accuracy, then discuss ConDet's relation to other work and outline directions for future studies. | [
430,
634,
2342
] | Train |
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