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294 | 4 | Title: References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages
Abstract: Miller, G. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2):81-97. Schmidhuber, J. (1990b). Towards compositional learning with dynamic neural networks. Technical Report FKI-129-90, Technische Universitat Munchen, Institut fu Informatik. Servan-Schreiber, D., Cleermans, A., and McClelland, J. (1988). Encoding sequential structure in simple recurrent networks. Technical Report CMU-CS-88-183, Carnegie Mellon University, Computer Science Department. | [
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633,
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638,
665,
699,
807,
1353,
1438,
1672,
2430,
2476
] | Train |
295 | 4 | Title: A Neuro-Dynamic Programming Approach to Retailer Inventory Management 1
Abstract: Miller, G. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2):81-97. Schmidhuber, J. (1990b). Towards compositional learning with dynamic neural networks. Technical Report FKI-129-90, Technische Universitat Munchen, Institut fu Informatik. Servan-Schreiber, D., Cleermans, A., and McClelland, J. (1988). Encoding sequential structure in simple recurrent networks. Technical Report CMU-CS-88-183, Carnegie Mellon University, Computer Science Department. | [
82,
197,
210,
220,
471,
565,
1012,
1316,
1782,
2078
] | Train |
296 | 6 | Title: Lookahead and Pathology in Decision Tree Induction
Abstract: The standard approach to decision tree induction is a top-down, greedy algorithm that makes locally optimal, irrevocable decisions at each node of a tree. In this paper, we empirically study an alternative approach, in which the algorithms use one-level lookahead to decide what test to use at a node. We systematically compare, using a very large number of real and artificial data sets, the quality of decision trees induced by the greedy approach to that of trees induced using lookahead. The main observations from our experiments are: (i) the greedy approach consistently produced trees that were just as accurate as trees produced with the much more expensive lookahead step; and (ii) we observed many instances of pathology, i.e., lookahead produced trees that were both larger and less accurate than trees produced without it. | [
227,
438,
638,
692,
1236
] | Train |
297 | 2 | Title: Automatic Feature Extraction in Machine Learning
Abstract: This thesis presents a machine learning model capable of extracting discrete classes out of continuous valued input features. This is done using a neurally inspired novel competitive classifier (CC) which feeds the discrete classifications forward to a supervised machine learning model. The supervised learning model uses the discrete classifications and perhaps other information available to solve a problem. The supervised learner then generates feedback to guide the CC into potentially more useful classifications of the continuous valued input features. Two supervised learning models are combined with the CC creating ASOCS-AFE and ID3-AFE. Both models are simulated and the results are analyzed. Based on these results, several areas of future research are proposed. | [
441,
809,
1321
] | Test |
298 | 4 | Title: How to Dynamically Merge Markov Decision Processes
Abstract: We are frequently called upon to perform multiple tasks that compete for our attention and resource. Often we know the optimal solution to each task in isolation; in this paper, we describe how this knowledge can be exploited to efficiently find good solutions for doing the tasks in parallel. We formulate this problem as that of dynamically merging multiple Markov decision processes (MDPs) into a composite MDP, and present a new theoretically-sound dynamic programming algorithm for finding an optimal policy for the composite MDP. We analyze various aspects of our algorithm and Every day, we are faced with the problem of doing multiple tasks in parallel, each of which competes for our attention and resource. If we are running a job shop, we must decide which machines to allocate to which jobs, and in what order, so that no jobs miss their deadlines. If we are a mail delivery robot, we must find the intended recipients of the mail while simultaneously avoiding fixed obstacles (such as walls) and mobile obstacles (such as people), and still manage to keep ourselves sufficiently charged up. Frequently we know how to perform each task in isolation; this paper considers how we can take the information we have about the individual tasks and combine it to efficiently find an optimal solution for doing the entire set of tasks in parallel. More importantly, we describe a theoretically-sound algorithm for doing this merging dynamically; new tasks (such as a new job arrival at a job shop) can be assimilated online into the solution being found for the ongoing set of simultaneous tasks. illustrate its use on a simple merging problem. | [
410,
552
] | Train |
299 | 6 | Title: On the Approximability of Numerical Taxonomy (Fitting Distances by Tree Metrics)
Abstract: We consider the problem of fitting an n fi n distance matrix D by a tree metric T . Let " be the distance to the closest tree metric, that is, " = min T fk T; D k 1 g. First we present an O(n 2 ) algorithm for finding an additive tree T such that k T; D k 1 3", giving the first algorithm for this problem with a performance guarantee. Second we show that it is N P-hard to find a tree T such that k T; D k 1 < 9 | [
596,
746,
1827,
2083,
2110
] | Train |
300 | 0 | Title: Storing and Indexing Plan Derivations through Explanation-based Analysis of Retrieval Failures
Abstract: Case-Based Planning (CBP) provides a way of scaling up domain-independent planning to solve large problems in complex domains. It replaces the detailed and lengthy search for a solution with the retrieval and adaptation of previous planning experiences. In general, CBP has been demonstrated to improve performance over generative (from-scratch) planning. However, the performance improvements it provides are dependent on adequate judgements as to problem similarity. In particular, although CBP may substantially reduce planning effort overall, it is subject to a mis-retrieval problem. The success of CBP depends on these retrieval errors being relatively rare. This paper describes the design and implementation of a replay framework for the case-based planner dersnlp+ebl. der-snlp+ebl extends current CBP methodology by incorporating explanation-based learning techniques that allow it to explain and learn from the retrieval failures it encounters. These techniques are used to refine judgements about case similarity in response to feedback when a wrong decision has been made. The same failure analysis is used in building the case library, through the addition of repairing cases. Large problems are split and stored as single goal subproblems. Multi-goal problems are stored only when these smaller cases fail to be merged into a full solution. An empirical evaluation of this approach demonstrates the advantage of learning from experienced retrieval failure. | [
593,
1621
] | Train |
301 | 2 | Title: Data Exploration with Reflective Adaptive Models
Abstract: Case-Based Planning (CBP) provides a way of scaling up domain-independent planning to solve large problems in complex domains. It replaces the detailed and lengthy search for a solution with the retrieval and adaptation of previous planning experiences. In general, CBP has been demonstrated to improve performance over generative (from-scratch) planning. However, the performance improvements it provides are dependent on adequate judgements as to problem similarity. In particular, although CBP may substantially reduce planning effort overall, it is subject to a mis-retrieval problem. The success of CBP depends on these retrieval errors being relatively rare. This paper describes the design and implementation of a replay framework for the case-based planner dersnlp+ebl. der-snlp+ebl extends current CBP methodology by incorporating explanation-based learning techniques that allow it to explain and learn from the retrieval failures it encounters. These techniques are used to refine judgements about case similarity in response to feedback when a wrong decision has been made. The same failure analysis is used in building the case library, through the addition of repairing cases. Large problems are split and stored as single goal subproblems. Multi-goal problems are stored only when these smaller cases fail to be merged into a full solution. An empirical evaluation of this approach demonstrates the advantage of learning from experienced retrieval failure. | [
46,
238,
489
] | Validation |
302 | 5 | Title: Confidence Estimation for Speculation Control
Abstract: Modern processors improve instruction level parallelism by speculation. The outcome of data and control decisions is predicted, and the operations are speculatively executed and only committed if the original predictions were correct. There are a number of other ways that processor resources could be used, such as threading or eager execution. As the use of speculation increases, we believe more processors will need some form of speculation control to balance the benefits of speculation against other possible activities. Confidence estimation is one technique that can be exploited by architects for speculation control. In this paper, we introduce performance metrics to compare confidence estimation mechanisms, and argue that these metrics are appropriate for speculation control. We compare a number of confidence estimation mechanisms, focusing on mechanisms that have a small implementation cost and gain benefit by exploiting characteristics of branch predictors, such as clustering of mispredicted branches. We compare the performance of the different confidence estimation methods using detailed pipeline simulations. Using these simulations, we show how to improve some confidence estimators, providing better insight for future investigations comparing and applying confidence estimators. | [
428,
432,
598
] | Train |
303 | 0 | Title: Relating Relational Learning Algorithms
Abstract: Relational learning algorithms are of special interest to members of the machine learning community; they offer practical methods for extending the representations used in algorithms that solve supervised learning tasks. Five approaches are currently being explored to address issues involved with using relational representations. This paper surveys algorithms embodying these approaches, summarizes their empirical evaluations, highlights their commonalities, and suggests potential directions for future research. | [
426,
478,
1174,
2091
] | Train |
304 | 2 | Title: Boltzmann Machine learning using mean field theory and linear response correction
Abstract: We present a new approximate learning algorithm for Boltzmann Machines, using a systematic expansion of the Gibbs free energy to second order in the weights. The linear response correction to the correlations is given by the Hessian of the Gibbs free energy. The computational complexity of the algorithm is cubic in the number of neurons. We compare the performance of the exact BM learning algorithm with first order (Weiss) mean field theory and second order (TAP) mean field theory. The learning task consists of a fully connected Ising spin glass model on 10 neurons. We conclude that 1) the method works well for paramagnetic problems 2) the TAP correction gives a significant improvement over the Weiss mean field theory, both for paramagnetic and spin glass problems and 3) that the inclusion of diagonal weights improves the Weiss approximation for paramagnetic problems, but not for spin glass problems. | [
108,
250,
427,
1461,
1912
] | Validation |
305 | 4 | Title: Solving Combinatorial Optimization Tasks by Reinforcement Learning: A General Methodology Applied to Resource-Constrained Scheduling
Abstract: This paper introduces a methodology for solving combinatorial optimization problems through the application of reinforcement learning methods. The approach can be applied in cases where several similar instances of a combinatorial optimization problem must be solved. The key idea is to analyze a set of "training" problem instances and learn a search control policy for solving new problem instances. The search control policy has the twin goals of finding high-quality solutions and finding them quickly. Results of applying this methodology to a NASA scheduling problem show that the learned search control policy is much more effective than the best known non-learning search procedure|a method based on simulated annealing. | [
82,
410,
552,
565
] | Train |
306 | 4 | Title: Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards
Abstract: Markov decision processes (MDPs) with undis-counted rewards represent an important class of problems in decision and control. The goal of learning in these MDPs is to find a policy that yields the maximum expected return per unit time. In large state spaces, computing these averages directly is not feasible; instead, the agent must estimate them by stochastic exploration of the state space. In this case, longer exploration times enable more accurate estimates and more informed decision-making. The learning curve for an MDP measures how the agent's performance depends on the allowed exploration time, T . In this paper we analyze these learning curves for a simple control problem with undiscounted rewards. In particular, methods from statistical mechanics are used to calculate lower bounds on the agent's performance in the thermodynamic limit T ! 1, N ! 1, ff = T =N (finite), where T is the number of time steps allotted per policy evaluation and N is the size of the state space. In this limit, we provide a lower bound on the return of policies that appear optimal based on imperfect statistics. | [
57,
552,
554,
565,
967,
1376
] | Train |
307 | 5 | Title: A Comparison of Full and Partial Predicated Execution Support for ILP Processors
Abstract: One can effectively utilize predicated execution to improve branch handling in instruction-level parallel processors. Although the potential benefits of predicated execution are high, the tradeoffs involved in the design of an instruction set to support predicated execution can be difficult. On one end of the design spectrum, architectural support for full predicated execution requires increasing the number of source operands for all instructions. Full predicate support provides for the most flexibility and the largest potential performance improvements. On the other end, partial predicated execution support, such as conditional moves, requires very little change to existing architectures. This paper presents a preliminary study to qualitatively and quantitatively address the benefit of full and partial predicated execution support. With our current compiler technology, we show that the compiler can use both partial and full predication to achieve speedup in large control-intensive programs. Some details of the code generation techniques are shown to provide insight into the benefit of going from partial to full predication. Preliminary experimental results are very encouraging: partial predication provides an average of 33% performance improvement for an 8-issue processor with no predicate support while full predication provides an additional 30% improvement. | [
598,
735
] | Test |
308 | 6 | Title: The Power of Self-Directed Learning
Abstract: This paper studies self-directed learning, a variant of the on-line learning model in which the learner selects the presentation order for the instances. We give tight bounds on the complexity of self-directed learning for the concept classes of monomials, k-term DNF formulas, and orthogonal rectangles in f0; 1; ; n1g d . These results demonstrate that the number of mistakes under self-directed learning can be surprisingly small. We then prove that the model of self-directed learning is more powerful than all other commonly used on-line and query learning models. Next we explore the relationship between the complexity of self-directed learning and the Vapnik-Chervonenkis dimension. Finally, we explore a relationship between Mitchell's version space algorithm and the existence of self-directed learning algorithms that make few mistakes. fl Supported in part by a GE Foundation Junior Faculty Grant and NSF Grant CCR-9110108. Part of this research was conducted while the author was at the M.I.T. Laboratory for Computer Science and supported by NSF grant DCR-8607494 and a grant from the Siemens Corporation. Net address: sg@cs.wustl.edu. | [
9,
1456,
2028
] | Train |
309 | 1 | Title: The Power of Self-Directed Learning
Abstract: A lower-bound result on the power of Abstract This paper presents a lower-bound result on the computational power of a genetic algorithm in the context of combinatorial optimization. We describe a new genetic algorithm, the merged genetic algorithm, and prove that for the class of monotonic functions, the algorithm finds the optimal solution, and does so with an exponential convergence rate. The analysis pertains to the ideal behavior of the algorithm where the main task reduces to showing convergence of probability distributions over the search space of combinatorial structures to the optimal one. We take exponential convergence to be indicative of efficient solvability for the sample-bounded algorithm, although a sampling theory is needed to better relate the limit behavior to actual behavior. The paper concludes with a discussion of some immediate problems that lie ahead. a genetic algorithm | [
163
] | Train |
310 | 2 | Title: Forecasting electricity demand using nonlinear mixture of experts
Abstract: In this paper we study a forecasting model based on mixture of experts for predicting the French electric daily consumption energy. We split the task into two parts. Using mixture of experts, a first model predicts the electricity demand from the exogenous variables (such as temperature and degree of cloud cover) and can be viewed as a nonlinear regression model of mixture of Gaussians. Using a single neural network, a second model predicts the evolution of the residual error of the first one, and can be viewed as an nonlinear autoregression model. We analyze the splitting of the input space generated by the mixture of experts model, and compare the performance to models presently used. | [
74,
668,
747,
2513
] | Train |
311 | 2 | Title: June 1994 T o app ear in Neural Computation A Coun terexample to T emp
Abstract: Sutton's TD( ) metho d aims to provide a represen tation of the cost function in an absorbing Mark ov chain with transition costs. A simple example is given where the represen tation obtained dep ends on . For = 1 the represen tation is optimal with resp ect to a least squares error criterion, but as decreases towards 0 the represen tation becomes progressiv ely worse and, in some cases, very poor. The example suggests a need to understand better the circumstances under which TD(0) and Q-learning obtain satisfactory neural net work-based compact represen tations of the cost function. A variation of TD(0) is also prop osed, which performs b etter on the example. | [
406,
552
] | Train |
312 | 3 | Title: Chain graphs for learning
Abstract: | [
427,
577,
645,
772
] | Train |
313 | 0 | Title: The Case for Graph-Structured Representations
Abstract: Case-based reasoning involves reasoning from cases: specific pieces of experience, the reasoner's or another's, that can be used to solve problems. We use the term "graph-structured" for representations that (1) are capable of expressing the relations between any two objects in a case, (2) allow the set of relations used to vary from case to case, and (3) allow the set of possible relations to be expanded as necessary to describe new cases. Such representations can be implemented as, for example, semantic networks or lists of concrete propositions in some logic. We believe that graph-structured representations offer significant advantages, and thus we are investigating ways to implement such representations efficiently. We make a "case-based argument" using examples from two systems, chiron and caper, to show how a graph-structured representation supports two different kinds of case-based planning in two different domains. We discuss the costs associated with graph-structured representations and describe an approach to reducing those costs, imple mented in caper. | [
534,
801,
1354,
1377,
1642
] | Validation |
314 | 5 | Title: Employing Linear Regression in Regression Tree Leaves
Abstract: The advantage of using linear regression in the leaves of a regression tree is analysed in the paper. It is carried out how this modification affects the construction, pruning and interpretation of a regression tree. The modification is tested on artificial and real-life domains. The results show that the modification is beneficial as it leads to smaller classification errors of induced regression trees. Keywords: machine learning, TDIDT, regression, linear regression, Bayesian approach. | [
156,
509,
1073,
1244,
1596,
1684,
1726
] | Train |
315 | 2 | Title: Cortical Functionality Emergence: Self-Organization of Complex Structures: From Individual to Collective Dynamics,
Abstract: General Theory & Quantitative Results Abstract: The human genotype represents at most ten billion binary informations, whereas the human brain contains more than a million times a billion synapses. So a differentiated brain structure is essentially due to self-organization. Such self-organization is relevant for areas ranging from medicine to the design of intelligent complex systems. Many brain structures emerge as collective phenomenon of a microscopic neurosynaptic dynamics: a stochastic dynamics mimics the neuronal action potentials, while the synaptic dynamics is modeled by a local coupling dynamics of type Hebb-rule, that is, a synaptic efficiency increases after coincident spiking of pre- and postsynaptic neuron. The microscopic dynamics is transformed to a collective dynamics reminiscent of hydrodynamics. The theory models empirical findings quantitatively: Topology preserving neuronal maps were assumed by Descartes in 1664; their self-organization was suggested by Weiss in 1928; their empirical observation was reported by Marshall in 1941; it is shown that they are neurosynaptically stable due to ubiquitous infinitesimal short range electrical or chemical leakage. In the visual cortex, neuronal stimulus orientation preference emerges; empirically measured orientation patterns are determined by the Poisson equation of electrostatics; this Poisson equation orientation pattern emergence is derived here. Complex cognitive abilities emerge when the basic local synaptic changes are regulated by valuation, emergent valuation, attention, attention focus or combination of subnetworks. Altogether a general theory is presented for the emergence of functionality from synaptic growth in neuro-biological systems. The theory provides a transformation to a collective dynamics and is used for quantitative modeling of empirical data. | [
747
] | Train |
316 | 5 | Title: Cortical Functionality Emergence: Self-Organization of Complex Structures: From Individual to Collective Dynamics,
Abstract: A Methodology for Evaluating Theory Revision Systems: Results Abstract Theory revision systems are learning systems that have a goal of making small changes to an original theory to account for new data. A measure for the distance between two theories is proposed. This measure corresponds to the minimum number of edit operations at the literal level required to transform one theory into another. By computing the distance between an original theory and a revised theory, the claim that a theory revision system makes few revisions to a theory may be quantitatively evaluated. We present data using both accuracy and the distance metric on Audrey II, with Audrey II fl | [
344
] | Validation |
317 | 6 | Title: A dataset decomposition approach to data mining and machine discovery
Abstract: We present a novel data mining approach based on decomposition. In order to analyze a given dataset, the method decomposes it to a hierarchy of smaller and less complex datasets that can be analyzed independently. The method is experimentally evaluated on a real-world housing loans allocation dataset, showing that the decomposition can (1) discover meaningful intermediate concepts, (2) decompose a relatively complex dataset to datasets that are easy to analyze and comprehend, and (3) derive a classifier of high classification accuracy. We also show that human interaction has a positive effect on both the comprehensibility and classification accuracy. | [
417,
508,
2326
] | Train |
318 | 0 | Title: Generalizing from Case Studies: A Case Study
Abstract: Most empirical evaluations of machine learning algorithms are case studies evaluations of multiple algorithms on multiple databases. Authors of case studies implicitly or explicitly hypothesize that the pattern of their results, which often suggests that one algorithm performs significantly better than others, is not limited to the small number of databases investigated, but instead holds for some general class of learning problems. However, these hypotheses are rarely supported with additional evidence, which leaves them suspect. This paper describes an empirical method for generalizing results from case studies and an example application. This method yields rules describing when some algorithms significantly outperform others on some dependent measures. Advantages for generalizing from case studies and limitations of this particular approach are also described. | [
426,
445,
686,
991,
1173,
1644,
2310,
2333,
2427
] | Train |
319 | 6 | Title: Error Reduction through Learning Multiple Descriptions
Abstract: Learning multiple descriptions for each class in the data has been shown to reduce generalization error but the amount of error reduction varies greatly from domain to domain. This paper presents a novel empirical analysis that helps to understand this variation. Our hypothesis is that the amount of error reduction is linked to the "degree to which the descriptions for a class make errors in a correlated manner." We present a precise and novel definition for this notion and use twenty-nine data sets to show that the amount of observed error reduction is negatively correlated with the degree to which the descriptions make errors in a correlated manner. We empirically show that it is possible to learn descriptions that make less correlated errors in domains in which many ties in the search evaluation measure (e.g. information gain) are experienced during learning. The paper also presents results that help to understand when and why multiple descriptions are a help (irrelevant attributes) and when they are not as much help (large amounts of class noise). | [
29,
1273
] | Train |
320 | 2 | Title: Appears in Working Notes, Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms
Abstract: This paper presents the Plannett system, which combines artificial neural networks to achieve expert- level accuracy on the difficult scientific task of recognizing volcanos in radar images of the surface of the planet Venus. Plannett uses ANNs that vary along two dimensions: the set of input features used to train and the number of hidden units. The ANNs are combined simply by averaging their output activations. When Plannett is used as the classification module of a three-stage image analysis system called JAR- tool, the end-to-end accuracy (sensitivity and specificity) is as good as that of a human planetary geologist on a four-image test suite. JARtool-Plannett also achieves the best algorithmic accuracy on these images to date. | [
259
] | Train |
321 | 4 | Title: Planning with Closed-Loop Macro Actions
Abstract: Planning and learning at multiple levels of temporal abstraction is a key problem for artificial intelligence. In this paper we summarize an approach to this problem based on the mathematical framework of Markov decision processes and reinforcement learning. Conventional model-based reinforcement learning uses primitive actions that last one time step and that can be modeled independently of the learning agent. These can be generalized to macro actions, multi-step actions specified by an arbitrary policy and a way of completing. Macro actions generalize the classical notion of a macro operator in that they are closed loop, uncertain, and of variable duration. Macro actions are needed to represent common-sense higher-level actions such as going to lunch, grasping an object, or traveling to a distant city. This paper generalizes prior work on temporally abstract models (Sutton 1995) and extends it from the prediction setting to include actions, control, and planning. We define a semantics of models of macro actions that guarantees the validity of planning using such models. This paper present new results in the theory of planning with macro actions and illustrates its potential advantages in a gridworld task. | [
566,
1192,
1954,
2179,
2305
] | Test |
322 | 6 | Title: Statistical Tests for Comparing Supervised Classification Learning Algorithms
Abstract: This paper reviews five statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type 1 error). Two widely-used statistical tests are shown to have high probability of Type I error in certain situations and should never be used. These tests are (a) a test for the difference of two proportions and (b) a paired-differences t test based on taking several random train/test splits. A third test, a paired-differences t test based on 10-fold cross-validation, exhibits somewhat elevated probability of Type I error. A fourth test, McNemar's test, is shown to have low Type I error. The fifth test is a new test, 5x2cv, based on 5 iterations of 2-fold cross-validation. Experiments show that this test also has good Type I error. The paper also measures the power (ability to detect algorithm differences when they do exist) of these tests. The 5x2cv test is shown to be slightly more powerful than McNemar's test. The choice of the best test is determined by the computational cost of running the learning algorithm. For algorithms that can be executed only once, McNemar's test is the only test with acceptable Type I error. For algorithms that can be executed ten times, the 5x2cv test is recommended, because it is slightly more powerful and because it directly measures variation due to the choice of training set. | [
15,
160,
967,
1027,
1644,
2508
] | Train |
323 | 6 | Title: Learning Active Classifiers
Abstract: Many classification algorithms are "passive", in that they assign a class-label to each instance based only on the description given, even if that description is incomplete. In contrast, an active classifier can | at some cost | obtain the values of missing attributes, before deciding upon a class label. The expected utility of using an active classifier depends on both the cost required to obtain the additional attribute values and the penalty incurred if it outputs the wrong classification. This paper considers the problem of learning near-optimal active classifiers, using a variant of the probably-approximately-correct (PAC) model. After defining the framework | which is perhaps the main contribution of this paper | we describe a situation where this task can be achieved efficiently, but then show that the task is often intractable. | [
140,
228,
1322,
2467,
2560
] | Test |
324 | 3 | Title: BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE
Abstract: Probabilistic inference algorithms for belief updating, finding the most probable explanation, the maximum a posteriori hypothesis, and the maximum expected utility are reformulated within the bucket elimination framework. This emphasizes the principles common to many of the algorithms appearing in the probabilistic inference literature and clarifies the relationship of such algorithms to nonserial dynamic programming algorithms. A general method for combining conditioning and bucket elimination is also presented. For all the algorithms, bounds on complexity are given as a function of the problem's structure. | [
62,
185,
278,
326,
327,
332,
389
] | Test |
325 | 3 | Title: Bayesian Model Selection in Social Research (with Discussion by
Abstract: 1 This article will be published in Sociological Methodology 1995, edited by Peter V. Marsden, Cambridge, Mass.: Blackwells. Adrian E. Raftery is Professor of Statistics and Sociology, Department of Sociology, DK-40, University of Washington, Seattle, WA 98195. This research was supported by NIH grant no. 5R01HD26330. I would like to thank Robert Hauser, Michael Hout, Steven Lewis, Scott Long, Diane Lye, Peter Marsden, Bruce Western, Yu Xie and two anonymous reviewers for detailed comments on an earlier version. I am also grateful to Clem Brooks, Sir David Cox, Tom DiPrete, John Goldthorpe, David Grusky, Jennifer Hoeting, Robert Kass, David Madigan, Michael Sobel and Chris Volinsky for helpful discussions and correspondence. | [
211,
1201
] | Test |
326 | 3 | Title: Topological Parameters for time-space tradeoff
Abstract: In this paper we propose a family of algorithms combining tree-clustering with conditioning that trade space for time. Such algorithms are useful for reasoning in probabilistic and deterministic networks as well as for accomplishing optimization tasks. By analyzing the problem structure it will be possible to select from a spectrum the algorithm that best meets a given time-space specifica tion. | [
278,
324,
327
] | Train |
327 | 3 | Title: Global Conditioning for Probabilistic Inference in Belief Networks
Abstract: In this paper we propose a new approach to probabilistic inference on belief networks, global conditioning, which is a simple generalization of Pearl's (1986b) method of loop-cutset conditioning. We show that global conditioning, as well as loop-cutset conditioning, can be thought of as a special case of the method of Lauritzen and Spiegelhalter (1988) as refined by Jensen et al (1990a; 1990b). Nonetheless, this approach provides new opportunities for parallel processing and, in the case of sequential processing, a tradeoff of time for memory. We also show how a hybrid method (Suermondt and others 1990) combining loop-cutset conditioning with Jensen's method can be viewed within our framework. By exploring the relationships between these methods, we develop a unifying framework in which the advantages of each approach can be combined successfully. | [
324,
326,
945,
1532,
1899
] | Test |
328 | 2 | Title: Associative memory using action potential timing
Abstract: The dynamics and collective properties of feedback networks with spiking neurons are investigated. Special emphasis is given to the potential computational role of subthreshold oscillations. It is shown that model systems with integrate-and-fire neurons can function as associative memories on two distinct levels. On the first level, binary patterns are represented by the spike activity | "to fire or not to fire." On the second level, analog patterns are encoded in the relative firing times between individual spikes or between spikes and an underlying subthreshold oscillation. Both coding schemes may coexist within the same network. The results suggest that cortical neurons may perform a broad spectrum of associative computations far beyond the scope of the traditional firing-rate picture. | [
747,
2619
] | Validation |
329 | 1 | Title: Simple Subpopulation Schemes
Abstract: This paper considers a new method for maintaining diversity by creating subpopulations in a standard generational evolutionary algorithm. Unlike other methods, it replaces the concept of distance between individuals with tag bits that identify the subpopulation to which an individual belongs. Two variations of this method are presented, illustrating the feasibility of this approach. | [
237
] | Test |
330 | 2 | Title: Local Feature Analysis: A general statistical theory for object representation
Abstract: This paper considers a new method for maintaining diversity by creating subpopulations in a standard generational evolutionary algorithm. Unlike other methods, it replaces the concept of distance between individuals with tag bits that identify the subpopulation to which an individual belongs. Two variations of this method are presented, illustrating the feasibility of this approach. | [
354,
359,
576,
731,
747
] | Train |
331 | 2 | Title: From Data Distributions to Regularization in Invariant Learning
Abstract: Ideally pattern recognition machines provide constant output when the inputs are transformed under a group G of desired invariances. These invariances can be achieved by enhancing the training data to include examples of inputs transformed by elements of G, while leaving the corresponding targets unchanged. Alternatively the cost function for training can include a regularization term that penalizes changes in the output when the input is transformed under the group. This paper relates the two approaches, showing precisely the sense in which the regularized cost function approximates the result of adding transformed (or distorted) examples to the training data. The cost function for the enhanced training set is equivalent to the sum of the original cost function plus a regularizer. For unbiased models, the regularizer reduces to the intuitively obvious choice - a term that penalizes changes in the output when the inputs are transformed under the group. For infinitesimal transformations, the coefficient of the regularization term reduces to the variance of the distortions introduced into the training data. This correspondence provides a simple bridge between the two approaches. | [
101,
179,
774
] | Validation |
332 | 3 | Title: Exploiting Causal Independence in Bayesian Network Inference
Abstract: A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A Bayesian network can be viewed as representing a factorization of a joint probability into the multiplication of a set of conditional probabilities. We present a notion of causal independence that enables one to further factorize the conditional probabilities into a combination of even smaller factors and consequently obtain a finer-grain factorization of the joint probability. The new formulation of causal independence lets us specify the conditional probability of a variable given its parents in terms of an associative and commutative operator, such as or, sum or max, on the contribution of each parent. We start with a simple algorithm VE for Bayesian network inference that, given evidence and a query variable, uses the factorization to find the posterior distribution of the query. We show how this algorithm can be extended to exploit causal independence. Empirical studies, based on the CPCS networks for medical diagnosis, show that this method is more efficient than previous methods and allows for inference in larger networks than previous algorithms. | [
62,
185,
324,
389,
1064
] | Train |
333 | 4 | Title: A Comparison of Action Selection Learning Methods
Abstract: Our goal is to develop a hybrid cognitive model of how humans acquire skills on complex cognitive tasks. We are pursuing this goal by designing hybrid computational architectures for the NRL Navigation task, which requires competent senso-rimotor coordination. In this paper, we empirically compare two methods for control knowledge acquisition (reinforcement learning and a novel variant of action models), as well as a hybrid of these methods, with human learning on this task. Our results indicate that the performance of our action models approach more closely approximates the rate of human learning on the task than does reinforcement learning or the hybrid. We also experimentally explore the impact of background knowledge on system performance. By adding knowledge used by the action models system to the benchmark reinforcement learner, we elevate its performance above that of the action models system. | [
463,
477,
565,
566
] | Train |
334 | 6 | Title: Improved Noise-Tolerant Learning and Generalized Statistical Queries
Abstract: The statistical query learning model can be viewed as a tool for creating (or demonstrating the existence of) noise-tolerant learning algorithms in the PAC model. The complexity of a statistical query algorithm, in conjunction with the complexity of simulating SQ algorithms in the PAC model with noise, determine the complexity of the noise-tolerant PAC algorithms produced. Although roughly optimal upper bounds have been shown for the complexity of statistical query learning, the corresponding noise-tolerant PAC algorithms are not optimal due to inefficient simulations. In this paper we provide both improved simulations and a new variant of the statistical query model in order to overcome these inefficiencies. We improve the time complexity of the classification noise simulation of statistical query algorithms. Our new simulation has a roughly optimal dependence on the noise rate. We also derive a simpler proof that statistical queries can be simulated in the presence of classification noise. This proof makes fewer assumptions on the queries themselves and therefore allows one to simulate more general types of queries. We also define a new variant of the statistical query model based on relative error, and we show that this variant is more natural and strictly more powerful than the standard additive error model. We demonstrate efficient PAC simulations for algorithms in this new model and give general upper bounds on both learning with relative error statistical queries and PAC simulation. We show that any statistical query algorithm can be simulated in the PAC model with malicious errors in such a way that the resultant PAC algorithm has a roughly optimal tolerable malicious error rate and sample complexity. Finally, we generalize the types of queries allowed in the statistical query model. We discuss the advantages of allowing these generalized queries and show that our results on improved simulations also hold for these queries. This paper is available from the Center for Research in Computing Technology, Division of Applied Sciences, Harvard University as technical report TR-17-94. | [
20,
267
] | Test |
335 | 5 | Title: Incremental Reduced Error Pruning
Abstract: This paper outlines some problems that may occur with Reduced Error Pruning in relational learning algorithms, most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning, is proposed that attempts to address all of these problems. Experiments show that in many noisy domains this method is much more efficient than alternative algorithms, along with a slight gain in accuracy. However, the experiments show as well that the use of the algorithm cannot be recommended for domains which require a very specific concept description. | [
344,
378,
426,
585
] | Train |
336 | 2 | Title: PREENS Tutorial How to use tools and NN simulations
Abstract: This report contains a description about how to use PREENS: its tools, convis and its neural network simulation programs. It does so by using several sample sessions. For more technical details, I refer to the convis technical description. | [
747
] | Train |
337 | 2 | Title: Meter as Mechanism: A Neural Network that Learns Metrical Patterns
Abstract: One kind of prosodic structure that apparently underlies both music and language is meter. Yet detailed measurements of both music and speech show that the nested periodicities that define metrical structure are noisy in some sense. What kind of system could produce or perceive such variable metrical timing? And what would it take to store particular metrical patterns in the long-term memory of the system? We have developed a network of coupled oscillators that both produces and perceives metrical patterns of pulses. In addition, beginning with an initial state with no biases, it learns to prefer 3-beat patterns (like waltzes) over 2-beat patterns. Models of this general class could learn to entrain to musical patterns. And given a way to process speech to extract appropriate pulses, the model should be applicable to metrical structure in speech as well. Is language metrical? Meter refers both to particular sorts of patterns in time and to an abstract description of such patterns, potentially a cognitive representation of them. In both cases there are two or more hierarchical levels at which equally spaced events occur, and the periods characterizing these levels are integral multiples of each other (usually 2 or 3). The hierarchy is implied in standard Western musical notation, where the different levels are indicated by kinds of notes (quarter notes, half notes, etc.) and by bars separating measures. For example, in a basic waltz-time meter, there are individual beats, all with the same spacing, grouped into sets of three with every third one receiving a stronger accent. In such a meter, there is a hierarchy consisting of both a faster periodic cycle (at the beat level) and a slower one (the measure level) that is 1/3 as fast with its onset (or zero phase angle) coinciding with the zero phase angle of every third beat. Metrical systems like this seem to underlie most forms of music around the world and are often said to underlie human speech as well (Jones, 1932; Martin, 1972). However, an awkward difficulty is that the definition employs the notion of an integer since data on both music and speech show clearly that the perfect temporal ratios predicted by such a definition are not observed in performance. In music performance, various kinds of systematic temporal deviations in the timing specified by musical notation are known to | [
77,
143,
346,
363
] | Validation |
338 | 2 | Title: Knowledge Integration and Rule Extraction in Neural Networks Ph.D. Proposal
Abstract: One kind of prosodic structure that apparently underlies both music and language is meter. Yet detailed measurements of both music and speech show that the nested periodicities that define metrical structure are noisy in some sense. What kind of system could produce or perceive such variable metrical timing? And what would it take to store particular metrical patterns in the long-term memory of the system? We have developed a network of coupled oscillators that both produces and perceives metrical patterns of pulses. In addition, beginning with an initial state with no biases, it learns to prefer 3-beat patterns (like waltzes) over 2-beat patterns. Models of this general class could learn to entrain to musical patterns. And given a way to process speech to extract appropriate pulses, the model should be applicable to metrical structure in speech as well. Is language metrical? Meter refers both to particular sorts of patterns in time and to an abstract description of such patterns, potentially a cognitive representation of them. In both cases there are two or more hierarchical levels at which equally spaced events occur, and the periods characterizing these levels are integral multiples of each other (usually 2 or 3). The hierarchy is implied in standard Western musical notation, where the different levels are indicated by kinds of notes (quarter notes, half notes, etc.) and by bars separating measures. For example, in a basic waltz-time meter, there are individual beats, all with the same spacing, grouped into sets of three with every third one receiving a stronger accent. In such a meter, there is a hierarchy consisting of both a faster periodic cycle (at the beat level) and a slower one (the measure level) that is 1/3 as fast with its onset (or zero phase angle) coinciding with the zero phase angle of every third beat. Metrical systems like this seem to underlie most forms of music around the world and are often said to underlie human speech as well (Jones, 1932; Martin, 1972). However, an awkward difficulty is that the definition employs the notion of an integer since data on both music and speech show clearly that the perfect temporal ratios predicted by such a definition are not observed in performance. In music performance, various kinds of systematic temporal deviations in the timing specified by musical notation are known to | [
627
] | Validation |
339 | 3 | Title: Abduction as Belief Revision
Abstract: We propose a model of abduction based on the revision of the epistemic state of an agent. Explanations must be sufficient to induce belief in the sentence to be explained (for instance, some observation), or ensure its consistency with other beliefs, in a manner that adequately accounts for factual and hypothetical sentences. Our model will generate explanations that nonmonotonically predict an observation, thus generalizing most current accounts, which require some deductive relationship between explanation and observation. It also provides a natural preference ordering on explanations, defined in terms of normality or plausibility. To illustrate the generality of our approach, we reconstruct two of the key paradigms for model-based diagnosis, abductive and consistency-based diagnosis, within our framework. This reconstruction provides an alternative semantics for both and extends these systems to accommodate our predictive explanations and semantic preferences on explanations. It also illustrates how more general information can be incorporated in a principled manner. fl Some parts of this paper appeared in preliminary form as Abduction as Belief Revision: A Model of Preferred Explanations, Proc. of Eleventh National Conf. on Artificial Intelligence (AAAI-93), Washington, DC, pp.642-648 (1993). | [
270,
342,
495,
1549,
1602
] | Train |
340 | 2 | Title: Best Probability of Activation and Performance Comparisons for Several Designs of Sparse Distributed Memory
Abstract: Report R95:09 ISRN : SICS-R-95/09-SE ISSN : 0283-3638 Abstract The optimal probability of activation and the corresponding performance is studied for three designs of Sparse Distributed Memory, namely, Kanerva's original design, Jaeckel's selected-coordinates design and Karlsson's modifi - cation of Jaeckel's design. We will assume that the hard locations (in Karlsson's case, the masks), the storage addresses and the stored data are randomly chosen, and we will consider different levels of random noise in the reading address. | [
341,
529,
709
] | Train |
341 | 2 | Title: Some Comments on the Information Stored in Sparse Distributed Memory
Abstract: Report R95:11 ISRN : SICS-R--95/11-SE ISSN : 0283-3638 Abstract We consider a sparse distributed memory with randomly chosen hard locations, in which an unknown number T of random data vectors have been stored. A method is given to estimate T from the content of the memory with high accuracy. In fact, our estimate is unbiased, the coefficient of variation being roughly inversely proportional to p MU , where M is the number of hard locations in the memory and U the length of data, so the accuracy can be made arbitrarily high by making the memory big enough. A consequence of this is that the good reading methods in [5] and [6] can be used without any need for the special extra location introduced there. | [
340,
529,
709
] | Train |
342 | 3 | Title: Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions.
Abstract: We describe a ranked-model semantics for if-then rules admitting exceptions, which provides a coherent framework for many facets of evidential and causal reasoning. Rule priorities are automatically extracted form the knowledge base to facilitate the construction and retraction of plausible beliefs. To represent causation, the formalism incorporates the principle of Markov shielding which imposes a stratified set of independence constraints on rankings of interpretations. We show how this formalism resolves some classical problems associated with specificity, prediction and abduction, and how it offers a natural way of unifying belief revision, belief update, and reasoning about actions. | [
270,
276,
339,
467,
495,
729,
776,
1800,
1945,
1993,
2016,
2546
] | Train |
343 | 1 | Title: A Promising genetic Algorithm Approach to Job-Shop Scheduling, Rescheduling, and Open-Shop Scheduling Problems
Abstract: We describe a ranked-model semantics for if-then rules admitting exceptions, which provides a coherent framework for many facets of evidential and causal reasoning. Rule priorities are automatically extracted form the knowledge base to facilitate the construction and retraction of plausible beliefs. To represent causation, the formalism incorporates the principle of Markov shielding which imposes a stratified set of independence constraints on rankings of interpretations. We show how this formalism resolves some classical problems associated with specificity, prediction and abduction, and how it offers a natural way of unifying belief revision, belief update, and reasoning about actions. | [
530,
1098,
1274,
1303,
1523,
1571,
1577
] | Test |
344 | 5 | Title: Quinlan, 1990 J.R. Quinlan. Learning logical definitions from relations. Machine Learning, First-order theory revision. In
Abstract: We describe a ranked-model semantics for if-then rules admitting exceptions, which provides a coherent framework for many facets of evidential and causal reasoning. Rule priorities are automatically extracted form the knowledge base to facilitate the construction and retraction of plausible beliefs. To represent causation, the formalism incorporates the principle of Markov shielding which imposes a stratified set of independence constraints on rankings of interpretations. We show how this formalism resolves some classical problems associated with specificity, prediction and abduction, and how it offers a natural way of unifying belief revision, belief update, and reasoning about actions. | [
1,
316,
335,
348,
521,
675,
963,
1007,
1244,
1260,
1267,
1275,
1312,
1442,
1445,
1622,
1627,
1671,
1881,
2032,
2171,
2215,
2229,
2290,
2291,
2339,
2426,
2441,
2589,
2609,
2617
] | Train |
345 | 3 | Title: On Convergence Properties of the EM Algorithm for Gaussian Mixtures
Abstract: We build up the mathematical connection between the "Expectation-Maximization" (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix P , and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of P and provide new results analyzing the effect that P has on the likelihood surface. Based on these mathematical results, we present a comparative discussion of the advantages and disadvantages of EM and other algorithms for the learning of Gaussian mixture models. This report describes research done at the Center for Biological and Computational Learning and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the Center is provided in part by a grant from the National Science Foundation under contract ASC-9217041. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00000-00-A-0000. The authors were also supported by the HK RGC Earmarked Grant CUHK250/94E, by a grant from the McDonnell-Pew Foundation, by a grant from ATR Human Information Processing Research Laboratories, by a grant from Siemens Corporation, and by grant N00014-90-1-0777 from the Office of Naval Research. Michael I. Jordan is an NSF Presidential Young Investigator. | [
74,
76,
117,
261,
622,
1924,
2266,
2389,
2421
] | Train |
346 | 2 | Title: PERCEPTION OF TIME AS PHASE: TOWARD AN ADAPTIVE-OSCILLATOR MODEL OF RHYTHMIC PATTERN PROCESSING 1
Abstract: We build up the mathematical connection between the "Expectation-Maximization" (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix P , and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of P and provide new results analyzing the effect that P has on the likelihood surface. Based on these mathematical results, we present a comparative discussion of the advantages and disadvantages of EM and other algorithms for the learning of Gaussian mixture models. This report describes research done at the Center for Biological and Computational Learning and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the Center is provided in part by a grant from the National Science Foundation under contract ASC-9217041. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00000-00-A-0000. The authors were also supported by the HK RGC Earmarked Grant CUHK250/94E, by a grant from the McDonnell-Pew Foundation, by a grant from ATR Human Information Processing Research Laboratories, by a grant from Siemens Corporation, and by grant N00014-90-1-0777 from the Office of Naval Research. Michael I. Jordan is an NSF Presidential Young Investigator. | [
132,
143,
163,
337,
363
] | Train |
347 | 3 | Title: A Reference Bayesian Test for Nested Hypotheses And its Relationship to the Schwarz Criterion
Abstract: We build up the mathematical connection between the "Expectation-Maximization" (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix P , and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of P and provide new results analyzing the effect that P has on the likelihood surface. Based on these mathematical results, we present a comparative discussion of the advantages and disadvantages of EM and other algorithms for the learning of Gaussian mixture models. This report describes research done at the Center for Biological and Computational Learning and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the Center is provided in part by a grant from the National Science Foundation under contract ASC-9217041. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00000-00-A-0000. The authors were also supported by the HK RGC Earmarked Grant CUHK250/94E, by a grant from the McDonnell-Pew Foundation, by a grant from ATR Human Information Processing Research Laboratories, by a grant from Siemens Corporation, and by grant N00014-90-1-0777 from the Office of Naval Research. Michael I. Jordan is an NSF Presidential Young Investigator. | [
84,
452,
713,
998,
999
] | Train |
348 | 5 | Title: First Order Regression: Applications in Real-World Domains
Abstract: A first order regression algorithm capable of handling real-valued (continuous) variables is introduced and some of its applications are presented. Regressional learning assumes real-valued class and discrete or real-valued variables. The algorithm combines regressional learning with standard ILP concepts, such as first order concept description and background knowledge. A clause is generated by successively refining the initial clause by adding literals of the form A = v for the discrete attributes, A v and A v for the real-valued attributes, and background knowledge literals to the clause body. The algorithm employs a covering approach (beam search), a heuristic impurity function, and stopping criteria based on local improvement, minimum number of examples, maximum clause length, minimum local improvement, minimum description length, allowed error, and variable depth. An outline of the algorithm and the results of the system's application in some artificial and real-world domains are presented. The real-world domains comprise: modelling of the water behavior in a surge tank, modelling of the workpiece roughness in a steel grinding process and modelling of the operator's behavior during the process of electrical discharge machining. Special emphasis is given to the evaluation of obtained models by domain experts and their comments on the aspects of practical use of the induced knowledge. The results obtained during the knowledge acquisition process show several important guidelines for knowledge acquisition, concerning mainly the process of interaction with domain experts, exposing primarily the importance of comprehensibility of the induced knowledge. | [
344,
638,
1244,
1596
] | Test |
349 | 2 | Title: Measuring Organization and Asymmetry in Bihemispheric Topographic Maps
Abstract: We address the problem of measuring the degree of hemispheric organization and asymmetry of organization in a computational model of a bihemispheric cerebral cortex. A theoretical framework for such measures is developed and used to produce algorithms for measuring the degree of organization, symmetry, and lateralization in topographic map formation. The performance of the resulting measures is tested for several topographic maps obtained by self-organization of an initially random network, and the results are compared with subjective assessments made by humans. It is found that the closest agreement with the human assessments is obtained by using organization measures based on sigmoid-type error averaging. Measures are developed which correct for large constant displacements as well as curving of the hemispheric topographic maps. | [
747
] | Validation |
350 | 2 | Title: Induction of Multiscale Temporal Structure
Abstract: Learning structure in temporally-extended sequences is a difficult computational problem because only a fraction of the relevant information is available at any instant. Although variants of back propagation can in principle be used to find structure in sequences, in practice they are not sufficiently powerful to discover arbitrary contingencies, especially those spanning long temporal intervals or involving high order statistics. For example, in designing a connectionist network for music composition, we have encountered the problem that the net is able to learn musical structure that occurs locally in time|e.g., relations among notes within a musical phrase|but not structure that occurs over longer time periods|e.g., relations among phrases. To address this problem, we require a means of constructing a reduced description of the sequence that makes global aspects more explicit or more readily detectable. I propose to achieve this using hidden units that operate with different time constants. Simulation experiments indicate that slower time-scale hidden units are able to pick up global structure, structure that simply can not be learned by standard Many patterns in the world are intrinsically temporal, e.g., speech, music, the unfolding of events. Recurrent neural net architectures have been devised to accommodate time-varying sequences. For example, the architecture shown in Figure 1 can map a sequence of inputs to a sequence of outputs. Learning structure in temporally-extended sequences is a difficult computational problem because the input pattern may not contain all the task-relevant information at any instant. Thus, back propagation. | [
111,
180,
201,
351,
427,
664,
730,
770
] | Test |
351 | 2 | Title: Sequence Learning with Incremental Higher-Order Neural Networks
Abstract: An incremental, higher-order, non-recurrent neural-network combines two properties found to be useful for sequence learning in neural-networks: higher-order connections and the incremental introduction of new units. The incremental, higher-order neural-network adds higher orders when needed by adding new units that dynamically modify connection weights. The new units modify the weights at the next time-step with information from the previous step. Since a theoretically unlimited number of units can be added to the network, information from the arbitrarily distant past can be brought to bear on each prediction. Temporal tasks can thereby be learned without the use of feedback, in contrast to recurrent neural-networks. Because there are no recurrent connections, training is simple and fast. Experiments have demonstrated speedups of two orders of magnitude over recurrent networks. | [
350,
730
] | Train |
352 | 3 | Title: Convergence controls for MCMC algorithms, with applications to hidden Markov chains
Abstract: In complex models like hidden Markov chains, the convergence of the MCMC algorithms used to approximate the posterior distribution and the Bayes estimates of the parameters of interest must be controlled in a robust manner. We propose in this paper a series of on-line controls, which rely on classical non-parametric tests, to evaluate independence from the start-up distribution, stability of the Markov chain, and asymptotic normality. These tests lead to graphical control spreadsheets which are presented in the set-up of normal mixture hidden Markov chains to compare the full Gibbs sampler with an aggregated Gibbs sampler based on the forward-backward formulae. | [
41,
904,
1372
] | Train |
353 | 2 | Title: Application of Neural Networks for the Classification of Diffuse Liver Disease by Quantitative Echography
Abstract: Three different methods were investigated to determine their ability to detect and classify various categories of diffuse liver disease. A statistical method, i.e., discriminant analysis, a supervised neural network called backpropagation and a nonsupervised, self-organizing feature map were examined. The investigation was performed on the basis of a previously selected set of acoustic and image texture parameters. The limited number of patients was successfully extended by generating additional but independent data with identical statistical properties. The generated data were used for training and test sets. The final test was made with the original patient data as a validation set. It is concluded that neural networks are an attractive alternative to traditional statistical techniques when dealing with medical detection and classification tasks. Moreover, the use of generated data for training the networks and the discriminant classifier has been shown to be justified and profitable. | [
427,
747
] | Train |
354 | 2 | Title: Principal and Independent Components in Neural Networks Recent Developments
Abstract: Nonlinear extensions of one-unit and multi-unit Principal Component Analysis (PCA) neural networks, introduced earlier by the authors, are reviewed. The networks and their nonlinear Hebbian learning rules are related to other signal expansions like Projection Pursuit (PP) and Independent Component Analysis (ICA). Separation results for mixtures of real world signals and im ages are given. | [
330,
576,
839,
1072,
1520
] | Train |
355 | 2 | Title: Generalization and Exclusive Allocation of Credit in Unsupervised Category Learning
Abstract: Acknowledgements: This research was supported in part by the Office of Naval Research (Cognitive and Neural Sciences, N00014-93-1-0208) and by the Whitaker Foundation (Special Opportunity Grant). We thank George Kalarickal, Charles Schmitt, William Ross, and Douglas Kelly for valuable discussions. | [
576,
745,
1093,
1094,
1562,
2068
] | Train |
356 | 2 | Title: A Flexible Model For Human Circadian Rhythms
Abstract: Many hormones and other physiological processes vary in a circadian pattern. Although a sine/cosine function can be used to model these patterns, this functional form is not appropriate when there is asymmetry between the peak and nadir phases. In this paper we describe a semi-parametric periodic spline function that can be fit to circadian rhythms. The model includes both phase and amplitude so that the time and the magnitude of the peak or nadir can be estimated. We also describe tests of fit for components in the model. Data from an experiment to study immunological responses in humans are used to demonstrate the methods. | [
190,
510
] | Validation |
357 | 1 | Title: Genetic Algorithms as Multi-Coordinators in Large-Scale Optimization
Abstract: We present high-level, decomposition-based algorithms for large-scale block-angular optimization problems containing integer variables, and demonstrate their effectiveness in the solution of large-scale graph partitioning problems. These algorithms combine the subproblem-coordination paradigm (and lower bounds) of price-directive decomposition methods with knapsack and genetic approaches to the utilization of "building blocks" of partial solutions. Even for graph partitioning problems requiring billions of variables in a standard 0-1 formulation, this approach produces high-quality solutions (as measured by deviations from an easily computed lower bound), and substantially outperforms widely-used graph partitioning techniques based on heuristics and spectral methods. | [
243,
537,
803,
1563,
2089
] | Train |
358 | 3 | Title: Hierarchical Spatio-Temporal Mapping of Disease Rates
Abstract: Maps of regional morbidity and mortality rates are useful tools in determining spatial patterns of disease. Combined with socio-demographic census information, they also permit assessment of environmental justice, i.e., whether certain subgroups suffer disproportionately from certain diseases or other adverse effects of harmful environmental exposures. Bayes and empirical Bayes methods have proven useful in smoothing crude maps of disease risk, eliminating the instability of estimates in low-population areas while maintaining geographic resolution. In this paper we extend existing hierarchical spatial models to account for temporal effects and spatio-temporal interactions. Fitting the resulting highly-parametrized models requires careful implementation of Markov chain Monte Carlo (MCMC) methods, as well as novel techniques for model evaluation and selection. We illustrate our approach using a dataset of county-specific lung cancer rates in the state of Ohio during the period 1968-1988. | [
95,
1255
] | Train |
359 | 2 | Title: Feature Extraction Using an Unsupervised Neural Network
Abstract: A novel unsupervised neural network for dimensionality reduction that seeks directions emphasizing multimodality is presented, and its connection to exploratory projection pursuit methods is discussed. This leads to a new statistical insight into the synaptic modification equations governing learning in Bienenstock, Cooper, and Munro (BCM) neurons (1982). The importance of a dimensionality reduction principle based solely on distinguishing features is demonstrated using a phoneme recognition experiment. The extracted features are compared with features extracted using a back-propagation network. | [
203,
330,
808,
1068,
1320,
1342,
1787,
1871,
2322,
2376,
2422,
2498,
2499,
2505,
2567
] | Train |
360 | 2 | Title: Investigating the Value of a Good Input Representation
Abstract: This paper is reprinted from Computational Learning Theory and Natural Learning Systems, vol. 3, T. Petsche, S. Judd, and S. Hanson, (eds.), forthcoming 1995. Copyrighted 1995 by MIT Press Abstract The ability of an inductive learning system to find a good solution to a given problem is dependent upon the representation used for the features of the problem. A number of factors, including training-set size and the ability of the learning algorithm to perform constructive induction, can mediate the effect of an input representation on the accuracy of a learned concept description. We present experiments that evaluate the effect of input representation on generalization performance for the real-world problem of finding genes in DNA. Our experiments that demonstrate that: (1) two different input representations for this task result in significantly different generalization performance for both neural networks and decision trees; and (2) both neural and symbolic methods for constructive induction fail to bridge the gap between these two representations. We believe that this real-world domain provides an interesting challenge problem for the machine learning subfield of constructive induction because the relationship between the two representations is well known, and because conceptually, the representational shift involved in constructing the better representation should not be too imposing. | [
151,
698
] | Train |
361 | 1 | Title: Overview of Selection Schemes and a Suggested Classification
Abstract: In this paper we emphasize the role of selection in evolutionary algorithms. We briefly review some of the most common selection schemes from the fields of Genetic Algorithms, Evolution Strategies and Genetic Programming. However we do not classify selection schemes according to which group of evolutionary algorithm they belong to, but rather distinguish between parent selection schemes, global competition and replacement schemes, and local competition and replacement schemes. This paper does not intend to fully review and analyse each of the presented selection schemes but tries to be a short reference for standard and some more advanced selection schemes. | [
55
] | Train |
362 | 2 | Title: Learning Topology-Preserving Maps Using Self-Supervised Backpropagation
Abstract: Self-supervised backpropagation is an unsupervised learning procedure for feedforward networks, where the desired output vector is identical with the input vector. For backpropagation, we are able to use powerful simulators running on parallel machines. Topology-preserving maps, on the other hand, can be developed by a variant of the competitive learning procedure. However, in a degenerate case, self-supervised backpropagation is a version of competitive learning. A simple extension of the cost function of backpropagation leads to a competitive version of self-supervised backpropagation, which can be used to produce topographic maps. We demonstrate the approach applied to the Traveling Salesman Problem (TSP). | [
747,
2191
] | Train |
363 | 2 | Title: Representing Rhythmic Patterns in a Network of Oscillators
Abstract: This paper describes an evolving computational model of the perception and production of simple rhythmic patterns. The model consists of a network of oscillators of different resting frequencies which couple with input patterns and with each other. Oscillators whose frequencies match periodicities in the input tend to become activated. Metrical structure is represented explicitly in the network in the form of clusters of oscillators whose frequencies and phase angles are constrained to maintain the harmonic relationships that characterize meter. Rests in rhythmic patterns are represented by explicit rest oscillators in the network, which become activated when an expected beat in the pattern fails to appear. The model makes predictions about the relative difficulty of The nested periodicity that defines musical, and probably also linguistic, meter appears to be fundamental to the way in which people perceive and produce patterns in time. Meter by itself, however, is not sufficient to describe patterns which are interesting or memorable because of how they deviate from the metrical hierarchy. The simplest deviations are rests or gaps where one or more levels in the hierarchy would normally have a beat. When beats are removed at regular intervals which match the period of some level of the metrical hierarchy, we have what we will call a simple rhythmic pattern. Figure 1 shows an example of a simple rhythmic pattern. Below it is a grid representation of the meter which is behind the pattern. patterns and the effect of deviations from periodicity in the input. | [
143,
337,
346
] | Validation |
364 | 2 | Title: Radial Basis Functions: L p -approximation orders with scattered centres
Abstract: In this paper we generalize several results on uniform approximation orders with radial basis functions in (Buhmann, Dyn and Levin, 1993) and (Dyn and Ron, 1993) to L p -approximation orders. These results apply, in particular, to approximants from spaces spanned by translates of radial basis functions by scattered centres. Examples to which our results apply include quasi-interpolation and least-squares approximation from radial function spaces. | [
365,
366,
590
] | Train |
365 | 2 | Title: Radial basis function approximation: from gridded centers to scattered centers
Abstract: The paper studies L 1 (IR d )-norm approximations from a space spanned by a discrete set of translates of a basis function . Attention here is restricted to functions whose Fourier transform is smooth on IR d n0, and has a singularity at the origin. Examples of such basis functions are the thin-plate splines and the multiquadrics, as well as other types of radial basis functions that are employed in Approximation Theory. The above approximation problem is well-understood in case the set of points ffi used for translating forms a lattice in IR d , and many optimal and quasi-optimal approximation schemes can already be found in the literature. In contrast, only few, mostly specific, results are known for a set ffi of scattered points. The main objective of this paper is to provide a general tool for extending approximation schemes that use integer translates of a basis function to the non-uniform case. We introduce a single, relatively simple, conversion method that preserves the approximation orders provided by a large number of schemes presently in the literature (more precisely, to almost all "stationary schemes"). In anticipation of future introduction of new schemes for uniform grids, an effort is made to impose only a few mild conditions on the function , which still allow for a unified error analysis to hold. In the course of the discussion here, the recent results of [BuDL] on scattered center approximation are reproduced and improved upon. | [
364,
590,
2112,
2572
] | Test |
366 | 2 | Title: AN UPPER BOUND ON THE APPROXIMATION POWER OF PRINCIPAL SHIFT-INVARIANT SPACES
Abstract: An upper bound on the L p -approximation power (1 p 1) provided by principal shift-invariant spaces is derived with only very mild assumptions on the generator. It applies to both stationary and non-stationary ladders, and is shown to apply to spaces generated by (exponential) box splines, polyharmonic splines, multiquadrics, and Gauss kernel. | [
364,
590
] | Validation |
367 | 4 | Title: Machine Learning, Explanation-Based Learning and Reinforcement Learning: A Unified View
Abstract: In speedup-learning problems, where full descriptions of operators are known, both explanation-based learning (EBL) and reinforcement learning (RL) methods can be applied. This paper shows that both methods involve fundamentally the same process of propagating information backward from the goal toward the starting state. Most RL methods perform this propagation on a state-by-state basis, while EBL methods compute the weakest preconditions of operators, and hence, perform this propagation on a region-by-region basis. Barto, Bradtke, and Singh (1995) have observed that many algorithms for reinforcement learning can be viewed as asynchronous dynamic programming. Based on this observation, this paper shows how to develop dynamic programming versions of EBL, which we call region-based dynamic programming or Explanation-Based Reinforcement Learning (EBRL). The paper compares batch and online versions of EBRL to batch and online versions of point-based dynamic programming and to standard EBL. The results show that region-based dynamic programming combines the strengths of EBL (fast learning and the ability to scale to large state spaces) with the strengths of reinforcement learning algorithms (learning of optimal policies). Results are shown in chess endgames and in synthetic maze tasks. | [
440,
483,
552,
565
] | Train |
368 | 2 | Title: Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification
Abstract: In this paper we present some extensions to the k-means algorithm for vector quantization that permit its efficient use in image segmentation and pattern classification tasks. It is shown that by introducing state variables that correspond to certain statistics of the dynamic behavior of the algorithm, it is possible to find the representative centers of the lower dimensional manifolds that define the boundaries between classes, for clouds of multi-dimensional, multi-class data; this permits one, for example, to find class boundaries directly from sparse data (e.g., in image segmentation tasks) or to efficiently place centers for pattern classification (e.g., with local Gaussian classifiers). The same state variables can be used to define algorithms for determining adaptively the optimal number of centers for clouds of data with space-varying density. Some examples of the application of these extensions are also given. This report describes research done within CIMAT (Guanajuato, Mexico), the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences, and at the Artificial Intelligence Laboratory. This research is sponsored by grants from the Office of Naval Research under contracts N00014-91-J-1270 and N00014-92-J-1879; by a grant from the National Science Foundation under contract ASC-9217041; and by a grant from the National Institutes of Health under contract NIH 2-S07-RR07047. Additional support is provided by the North Atlantic Treaty Organization, ATR Audio and Visual Perception Research Laboratories, Mitsubishi Electric Corporation, Sumitomo Metal Industries, and Siemens AG. Support for the A.I. Laboratory's artificial intelligence research is provided by ONR contract N00014-91-J-4038. J.L. Marroquin was supported in part by a grant from the Consejo Nacional de Ciencia y Tecnologia, Mexico. | [
611,
747
] | Test |
369 | 2 | Title: Limitations of self-organizing maps for vector quantization and multidimensional scaling
Abstract: The limitations of using self-organizing maps (SOM) for either clustering/vector quantization (VQ) or multidimensional scaling (MDS) are being discussed by reviewing recent empirical findings and the relevant theory. SOM's remaining ability of doing both VQ and MDS at the same time is challenged by a new combined technique of online K-means clustering plus Sammon mapping of the cluster centroids. SOM are shown to perform significantly worse in terms of quantization error, in recovering the structure of the clusters and in preserving the topology in a comprehensive empirical study using a series of multivariate normal clustering problems. | [
747
] | Train |
370 | 4 | Title: Robust Reinforcement Learning in Motion Planning
Abstract: While exploring to find better solutions, an agent performing online reinforcement learning (RL) can perform worse than is acceptable. In some cases, exploration might have unsafe, or even catastrophic, results, often modeled in terms of reaching `failure' states of the agent's environment. This paper presents a method that uses domain knowledge to reduce the number of failures during exploration. This method formulates the set of actions from which the RL agent composes a control policy to ensure that exploration is conducted in a policy space that excludes most of the unacceptable policies. The resulting action set has a more abstract relationship to the task being solved than is common in many applications of RL. Although the cost of this added safety is that learning may result in a suboptimal solution, we argue that this is an appropriate tradeoff in many problems. We illustrate this method in the domain of motion planning. | [
552,
562,
875
] | Validation |
371 | 3 | Title: Selecting Input Variables Using Mutual Information and Nonparametric Density Estimation
Abstract: In learning problems where a connectionist network is trained with a finite sized training set, better generalization performance is often obtained when unneeded weights in the network are eliminated. One source of unneeded weights comes from the inclusion of input variables that provide little information about the output variables. We propose a method for identifying and eliminating these input variables. The method first determines the relationship between input and output variables using nonparametric density estimation and then measures the relevance of input variables using the information theoretic concept of mutual information. We present results from our method on a simple toy problem and a nonlinear time series. | [
88,
157,
2507
] | Validation |
372 | 1 | Title: Investigating the role of diploidy in simulated populations of evolving individuals
Abstract: In most work applying genetic algorithms to populations of neural networks there is no real distinction between genotype and phenotype. In nature both the information contained in the genotype and the mapping of the genetic information into the phenotype are usually much more complex. The genotypes of many organisms exhibit diploidy, i.e., they include two copies of each gene: if the two copies are not identical in their sequences and therefore have a functional difference in their products (usually proteins), the expressed phenotypic feature is termed the dominant one, the other one recessive (not expressed). In this paper we review the literature on the use of diploidy and dominance operators in genetic algorithms; we present the new results we obtained with our own simulations in changing environments; finally, we discuss some results of our simulations that parallel biological findings. | [
38,
273
] | Train |
373 | 5 | Title: Task Selection for a Multiscalar Processor
Abstract: The Multiscalar architecture advocates a distributed processor organization and task-level speculation to exploit high degrees of instruction level parallelism (ILP) in sequential programs without impeding improvements in clock speeds. The main goal of this paper is to understand the key implications of the architectural features of distributed processor organization and task-level speculation for compiler task selection from the point of view of performance. We identify the fundamental performance issues to be: control ow speculation, data communication, data dependence speculation, load imbalance, and task overhead. We show that these issues are intimately related to a few key characteristics of tasks: task size, inter-task control ow, and inter-task data dependence. We describe compiler heuristics to select tasks with favorable characteristics. We report experimental results to show that the heuristics are successful in boosting overall performance by establishing larger ILP windows. | [
86,
652
] | Test |
374 | 4 | Title: An Introspection Approach to Querying a Trainer
Abstract: Technical Report 96-13 January 22, 1996 Abstract This paper introduces the Introspection Approach, a method by which a learning agent employing reinforcement learning can decide when to ask a training agent for instruction. When using our approach, we find that the same number of trainer's responses produced significantly faster learners than by having the learner ask for aid randomly. Guidance received via our approach is more informative than random guidance. Thus, we can reduce the interaction that the training agent has with the learning agent without reducing the speed with which the learner develops its policy. In fact, by being intelligent about when the learner asks for help, we can even increase the learning speed for the same level of trainer interaction. | [
455,
552
] | Validation |
375 | 6 | Title: Constructive Induction Using a Non-Greedy Strategy for Feature Selection
Abstract: We present a method for feature construction and selection that finds a minimal set of conjunctive features that are appropriate to perform the classification task. For problems where this bias is appropriate, the method outperforms other constructive induction algorithms and is able to achieve higher classification accuracy. The application of the method in the search for minimal multi-level boolean expressions is presented and analyzed with the help of some examples. | [
635,
638,
836,
1576
] | Test |
376 | 3 | Title: Bayesian Finite Mixtures for Nonlinear Modeling of Educational data
Abstract: In this paper we discuss a Bayesian approach for finding latent classes in the data. In our approach we use finite mixture models to describe the underlying structure in the data, and demonstrate that the possibility to use full joint probability models raises interesting new prospects for exploratory data analysis. The concepts and methods discussed are illustrated with a case study using a data set from a recent educational study. The Bayesian classification approach described has been implemented, and presents an appealing addition to the standard toolbox for exploratory data analysis of educational data. | [
558,
641,
704
] | Train |
377 | 2 | Title: Constructive Algorithms for Hierarchical Mixtures of Experts
Abstract: We present two additions to the hierarchical mixture of experts (HME) architecture. We view the HME as a tree structured classifier. Firstly, by applying a likelihood splitting criteria to each expert in the HME we "grow" the tree adaptively during training. Secondly, by considering only the most probable path through the tree we may "prune" branches away, either temporarily, or permanently if they become redundant. We demonstrate results for the growing and pruning algorithms which show significant speed ups and more efficient use of parameters over the conventional algorithms in discriminating between two interlocking spirals and classifying 8-bit parity patterns. | [
74,
622
] | Train |
378 | 6 | Title: Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine
Abstract: Ourston and Mooney, 1990b ] D. Ourston and R. J. Mooney. Improving shared rules in multiple category domain theories. Technical Report AI90-150, Artificial Intelligence Labora tory, University of Texas, Austin, TX, December 1990. | [
178,
218,
227,
286,
335,
396,
585,
692,
960,
1027,
1061,
1207,
1238,
1275,
1290,
1539,
1644,
1678,
1963,
2012,
2042,
2195,
2290,
2291,
2447,
2583
] | Train |
379 | 5 | Title: In: Machine Learning, Meta-reasoning and Logics, pp207-232, Learning from Imperfect Data
Abstract: Systems interacting with real-world data must address the issues raised by the possible presence of errors in the observations it makes. In this paper we first present a framework for discussing imperfect data and the resulting problems it may cause. We distinguish between two categories of errors in data random errors or `noise', and systematic errors and examine their relationship to the task of describing observations in a way which is also useful for helping in future problem-solving and learning tasks. Secondly we proceed to examine some of the techniques currently used in AI research for recognising such errors. | [
176,
756
] | Train |
380 | 1 | Title: Fitness Landscapes and Difficulty in Genetic Programming
Abstract: The structure of the fitness landscape on which genetic programming operates is examined. The landscapes of a range of problems of known difficulty are analyzed in an attempt to determine which landscape measures correlate with the difficulty of the problem. The autocorrelation of the fitness values of random walks, a measure which has been shown to be related to perceived difficulty using other techniques, is only a weak indicator of the difficulty as perceived by genetic programming. All of these problems show unusually low autocorrelation. Comparison of the range of landscape basin depths at the end of adaptive walks on the landscapes shows good correlation with problem difficulty, over the entire range of problems examined. | [
163,
188,
934,
1257,
1473,
1474,
1737,
1784,
2196,
2641
] | Validation |
381 | 6 | Title: Compression-Based Feature Subset Selection Keywords: Minimum Description Length Principle, Cross Validation, Noise
Abstract: Irrelevant and redundant features may reduce both predictive accuracy and comprehensibility of induced concepts. Most common Machine Learning approaches for selecting a good subset of relevant features rely on cross-validation. As an alternative, we present the application of a particular Minimum Description Length (MDL) measure to the task of feature subset selection. Using the MDL principle allows taking into account all of the available data at once. The new measure is information-theoretically plausible and yet still simple and therefore efficiently computable. We show empirically that this new method for judging the value of feature subsets is more efficient than and performs at least as well as methods based on cross-validation. Domains with both a large number of training examples and a large number of possible features yield the biggest gains in efficiency. Thus our new approach seems to scale up better to large learning problems than previous methods. | [
430,
635,
686,
2342
] | Train |
382 | 6 | Title: Learning Decision Lists Using Homogeneous Rules
Abstract: A decision list is an ordered list of conjunctive rules (?). Inductive algorithms such as AQ and CN2 learn decision lists incrementally, one rule at a time. Such algorithms face the rule overlap problem | the classification accuracy of the decision list depends on the overlap between the learned rules. Thus, even though the rules are learned in isolation, they can only be evaluated in concert. Existing algorithms solve this problem by adopting a greedy, iterative structure. Once a rule is learned, the training examples that match the rule are removed from the training set. We propose a novel solution to the problem: composing decision lists from homogeneous rules, rules whose classification accuracy does not change with their position in the decision list. We prove that the problem of finding a maximally accurate decision list can be reduced to the problem of finding maximally accurate homogeneous rules. We report on the performance of our algorithm on data sets from the UCI repository and on the MONK's problems. | [
29,
1236,
1837,
2132
] | Validation |
383 | 2 | Title: Constructing Fuzzy Graphs from Examples
Abstract: Methods to build function approximators from example data have gained considerable interest in the past. Especially methodologies that build models that allow an interpretation have attracted attention. Most existing algorithms, however, are either complicated to use or infeasible for high-dimensional problems. This article presents an efficient and easy to use algorithm to construct fuzzy graphs from example data. The resulting fuzzy graphs are based on locally independent fuzzy rules that operate solely on selected, important attributes. This enables the application of these fuzzy graphs also to problems in high dimensional spaces. Using illustrative examples and a real world data set it is demonstrated how the resulting fuzzy graphs offer quick insights into the structure of the example data, that is, the underlying model. | [
87,
631,
638
] | Train |
384 | 2 | Title: Hidden Markov Model Analysis of Motifs in Steroid Dehydrogenases and their Homologs
Abstract: Methods to build function approximators from example data have gained considerable interest in the past. Especially methodologies that build models that allow an interpretation have attracted attention. Most existing algorithms, however, are either complicated to use or infeasible for high-dimensional problems. This article presents an efficient and easy to use algorithm to construct fuzzy graphs from example data. The resulting fuzzy graphs are based on locally independent fuzzy rules that operate solely on selected, important attributes. This enables the application of these fuzzy graphs also to problems in high dimensional spaces. Using illustrative examples and a real world data set it is demonstrated how the resulting fuzzy graphs offer quick insights into the structure of the example data, that is, the underlying model. | [
14
] | Train |
385 | 4 | Title: Modeling the Student with Reinforcement Learning
Abstract: We describe a methodology for enabling an intelligent teaching system to make high level strategy decisions on the basis of low level student modeling information. This framework is less costly to construct, and superior to hand coding teaching strategies as it is more responsive to the learner's needs. In order to accomplish this, reinforcement learning is used to learn to associate superior teaching actions with certain states of the student's knowledge. Reinforcement learning (RL) has been shown to be flexible in handling noisy data, and does not need expert domain knowledge. A drawback of RL is that it often needs a significant number of trials for learning. We propose an off-line learning methodology using sample data, simulated students, and small amounts of expert knowledge to bypass this problem. | [
565,
567
] | Train |
386 | 2 | Title: Temporal Compositional Processing by a DSOM Hierarchical Model
Abstract: Any intelligent system, whether human or robotic, must be capable of dealing with patterns over time. Temporal pattern processing can be achieved if the system has a short-term memory capacity (STM) so that different representations can be maintained for some time. In this work we propose a neural model wherein STM is realized by leaky integrators in a self-organizing system. The model exhibits composition-ality, that is, it has the ability to extract and construct progressively complex and structured associations in an hierarchical manner, starting with basic and primitive (temporal) elements. | [
611,
745,
747
] | Train |
387 | 2 | Title: JUNG ET AL.: ESTIMATING ALERTNESS FORM THE EEG POWER SPECTRUM 1 Estimating Alertness from the
Abstract: In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traffic control to monitoring of nuclear power plants. Changes in the electroencephalographic (EEG) power spectrum accompany these fluctuations in the level of alertness, as assessed by measuring simultaneous changes in EEG and performance on an auditory monitoring task. By combining power spectrum estimation, principal component analysis and artificial neural networks, we show that continuous, accurate, noninvasive, and near real-time estimation of an operator's global level of alertness is feasible using EEG measures recorded from as few as two central scalp sites. This demonstration could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings. | [
293
] | Test |
388 | 2 | Title: Spatial-Temporal Analysis of Temperature Using Smoothing Spline ANOVA
Abstract: In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traffic control to monitoring of nuclear power plants. Changes in the electroencephalographic (EEG) power spectrum accompany these fluctuations in the level of alertness, as assessed by measuring simultaneous changes in EEG and performance on an auditory monitoring task. By combining power spectrum estimation, principal component analysis and artificial neural networks, we show that continuous, accurate, noninvasive, and near real-time estimation of an operator's global level of alertness is feasible using EEG measures recorded from as few as two central scalp sites. This demonstration could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings. | [
439,
2590
] | Test |
389 | 3 | Title: Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions
Abstract: This paper presents exact solutions and convergent approximations for inferences in Bayesian networks associated with finitely generated convex sets of distributions. Robust Bayesian inference is the calculation of bounds on posterior values given perturbations in a probabilistic model. The paper presents exact inference algorithms and analyzes the circumstances where exact inference becomes intractable. Two classes of algorithms for numeric approximations are developed through transformations on the original model. The first transformation reduces the robust inference problem to the estimation of probabilistic parameters in a Bayesian network. The second transformation uses Lavine's bracketing algorithm to generate a sequence of maximization problems in a Bayesian network. The analysis is extended to the *-contaminated, the lower density bounded, the belief function, the sub-sigma, the density bounded, the total variation and the density ratio classes of distributions. c fl1996 Carnegie Mellon University | [
185,
324,
332,
577,
1937
] | Validation |
390 | 1 | Title: Chaos, Fractals, and Genetic Algorithms
Abstract: This paper presents exact solutions and convergent approximations for inferences in Bayesian networks associated with finitely generated convex sets of distributions. Robust Bayesian inference is the calculation of bounds on posterior values given perturbations in a probabilistic model. The paper presents exact inference algorithms and analyzes the circumstances where exact inference becomes intractable. Two classes of algorithms for numeric approximations are developed through transformations on the original model. The first transformation reduces the robust inference problem to the estimation of probabilistic parameters in a Bayesian network. The second transformation uses Lavine's bracketing algorithm to generate a sequence of maximization problems in a Bayesian network. The analysis is extended to the *-contaminated, the lower density bounded, the belief function, the sub-sigma, the density bounded, the total variation and the density ratio classes of distributions. c fl1996 Carnegie Mellon University | [
145,
163
] | Train |
391 | 2 | Title: Geometry in Learning
Abstract: One of the fundamental problems in learning is identifying members of two different classes. For example, to diagnose cancer, one must learn to discriminate between benign and malignant tumors. Through examination of tumors with previously determined diagnosis, one learns some function for distinguishing the benign and malignant tumors. Then the acquired knowledge is used to diagnose new tumors. The perceptron is a simple biologically inspired model for this two-class learning problem. The perceptron is trained or constructed using examples from the two classes. Then the perceptron is used to classify new examples. We describe geometrically what a perceptron is capable of learning. Using duality, we develop a framework for investigating different methods of training a perceptron. Depending on how we define the "best" perceptron, different minimization problems are developed for training the perceptron. The effectiveness of these methods is evaluated empirically on four practical applications: breast cancer diagnosis, detection of heart disease, political voting habits, and sonar recognition. This paper does not assume prior knowledge of machine learning or pattern recognition. | [
142,
230,
427,
438,
1283
] | Train |
392 | 3 | Title: DRAFT Cluster-Weighted Modeling for Time Series Prediction and Characterization
Abstract: | [
76,
154
] | Train |
393 | 2 | Title: Density Networks and their Application to Protein Modelling
Abstract: I define a latent variable model in the form of a neural network for which only target outputs are specified; the inputs are unspecified. Although the inputs are missing, it is still possible to train this model by placing a simple probability distribution on the unknown inputs and maximizing the probability of the data given the parameters. The model can then discover for itself a description of the data in terms of an underlying latent variable space of lower dimensionality. I present preliminary results of the application of these models to protein data. | [
14,
157
] | Train |
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