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|---|---|---|---|---|
2,294 | 0 | Title: Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks
Abstract: We describe an integrated problem solving architecture named INBANCA in which Bayesian networks and case-based reasoning (CBR) work cooperatively on multiagent planning tasks. This includes two-team dynamic tasks, and... | [
66,
649,
1140,
2380,
2529
] | Train |
2,295 | 1 | Title: Diplomarbeit A Genetic Algorithm for the Topological Optimization of Neural Networks
Abstract: We describe an integrated problem solving architecture named INBANCA in which Bayesian networks and case-based reasoning (CBR) work cooperatively on multiagent planning tasks. This includes two-team dynamic tasks, an... | [
163,
427,
881,
2667
] | Train |
2,296 | 1 | Title: TECHNIQUES FOR REDUCING THE DISRUPTION OF SUPERIOR BUILDING BLOCKS IN GENETIC ALGORITHMS
Abstract: We describe an integrated problem solving architecture named INBANCA in which Bayesian networks and case-based reasoning (CBR) work cooperatively on multiagent planning tasks. This includes two-team dynamic tasks... | [
145,
163,
2248,
2251
] | Test |
2,297 | 6 | Title: Efficient Construction of Networks for Learned Representations with General to Specific Relationships
Abstract: Machine learning systems often represent concepts or rules as sets of attribute-value pairs. Many learning algorithms generalize or specialize these concept representations by removing or adding pair... | [
2304
] | Validation |
2,298 | 1 | Title: Convergence Analysis of Canonical Genetic Algorithms
Abstract: This paper analyzes the convergence properties of the canonical genetic algorithm (CGA) with mutation, crossover and proportional reproduction applied to static optimization problems. It is proved by means of homogeneous finite Markov chain analysi... | [
163,
2518
] | Train |
2,299 | 0 | Title: Case Retrieval Nets Applied to Large Case Bases
Abstract: This article presents some experimental results obtained from applying the Case Retrieval Net approach to large case bases. The obtained results suggest that CRNs can successfully handle case bases larger than considered in other reports. | [
75,
1854,
1855
] | Train |
2,300 | 5 | Title: Applying a Machine Learning Workbench: Experience with Agricultural Databases
Abstract: This paper reviews our experience with the application of machine learning techniques to agricultural databases. We have designed and implemented a machine learning workbench, WEKA, which permits rapid experimentation on a ... | [
479,
1337,
2636
] | Validation |
2,301 | 3 | Title: editors. Representing Preferences as Ceteris Paribus Comparatives
Abstract: Decision-theoretic preferences specify the relative desirability of all possible outcomes of alternative plans. In order to express general patterns of preference holding in a domain, we require a language that can refer directly to pr... | [
1901,
1907,
2588
] | Validation |
2,302 | 1 | Title: Genes, Phenes and the Baldwin Effect: Learning and Evolution in a Simulated Population
Abstract: The Baldwin Effect, first proposed in the late nineteenth century, suggests that the course of evolutionary change can be influenced by individually learned behavior. The existence of this effect is still a hotly d... | [
403,
1353,
1880,
2104,
2111
] | Train |
2,303 | 0 | Title: Case-based reactive navigation: A case-based method for on-line selection and adaptation of reactive control parameters
Abstract: This article presents a new line of research investigating on-line learning mechanisms for autonomous intelligent agents. We discuss a case-based method for dynamic selection and modi... | [
858,
1951,
2035
] | Train |
2,304 | 2 | Title: GENERALIZATION BY CONTROLLED EXPANSION OF EXAMPLES
Abstract: SG (Specific to General) is a learning system that derives general rules from specific examples. SG learns incrementally with good speed and generalization. The SG network is built of many simple nodes that adapt to the problem being learned. Learnin... | [
908,
2297
] | Test |
2,305 | 4 | Title: Analytical Mean Squared Error Curves for Temporal Difference Learning
Abstract: We provide analytical expressions governing changes to the bias and variance of the lookup table estimators provided by various Monte Carlo and temporal difference value estimation algorithms with o*ine updates over trials in absor... | [
321,
2150,
2183
] | Train |
2,306 | 2 | Title: On the Applicability of Neural Network and Machine Learning Methodologies to Natural Language Processing
Abstract: We examine the inductive inference of a complex grammar specifically, we consider the task of training a model to classify natural language sentences as grammatical or ungrammatical, thereby exhib... | [
427,
2049,
2594
] | Validation |
2,307 | 2 | Title: Parallel Gradient Distribution in Unconstrained Optimization
Abstract: A parallel version is proposed for a fundamental theorem of serial unconstrained optimization. The parallel theorem allows each of k parallel processors to use simultaneously a different algorithm, such as a descent, Newton, quasi-Newton or... | [
406,
1772,
1939
] | Train |
2,308 | 0 | Title: Problem Formulation, Program Synthesis and Program Transformation Techniques for Simulation, Optimization and Constraint Satisfaction (Research Statement)
Abstract: A parallel version is proposed for a fundamental theorem of serial unconstrained optimization. The parallel theorem allows each of k parallel proc... | [
240,
2652
] | Train |
2,309 | 4 | Title: EVOLVING SENSORS IN ENVIRONMENTS OF CONTROLLED COMPLEXITY
Abstract: 1 . Sensors represent a crucial link between the evolutionary forces shaping a species' relationship with its environment, and the individual's cognitive abilities to behave and learn. We report on experiments using a new class of "latent ener... | [
403,
538,
681,
1325,
1969
] | Validation |
2,310 | 0 | Title: Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1)
Abstract: This is a brief annotated bibliography that I wanted to make available to the attendees of my Machine Learning tutorial at the 1995 AI & Statistics Workshop. These slides are available in m... | [
66,
318,
2583,
2607
] | Train |
2,311 | 3 | Title: Bayesian MARS
Abstract: A Bayesian approach to multivariate adaptive regression spline (MARS) fitting (Friedman, 1991) is proposed. This takes the form of a probability distribution over the space of possible MARS models which is explored using reversible jump Markov chain Monte Carlo methods (Green, 1995). Th... | [
161,
2285,
2448
] | Train |
2,312 | 5 | Title: Theory-Guided Induction of Logic Programs by Inference of Regular Languages recursive clauses. merlin on the
Abstract: resent allowed sequences of resolution steps for the initial theory. There are, however, many characterizations of allowed sequences of resolution steps that cannot be expressed by a set of reso... | [
521,
1082,
1259,
2587
] | Train |
2,313 | 3 | Title: PERFECT SIMULATION OF CONDITIONALLY SPECIFIED MODELS
Abstract: We discuss how the ideas of producing perfect simulations based on coupling from the past for finite state space models naturally extend to mul-tivariate distributions with infinite or uncountable state spaces such as auto-gamma, auto-Poisson and a... | [
2208
] | Train |
2,314 | 2 | Title: GENERAL CLASSES OF CONTROL-LYAPUNOV FUNCTIONS
Abstract: The main result of this paper establishes the equivalence between null asymptotic controllability of nonlinear finite-dimensional control systems and the existence of continuous control-Lyapunov functions (clf's) defined by means of generalized derivative... | [
2187
] | Train |
2,315 | 6 | Title: Metric Entropy and Minimax Risk in Classification
Abstract: We apply recent results on the minimax risk in density estimation to the related problem of pattern classification. The notion of loss we seek to minimize is an information theoretic measure of how well we can predict the classification of future exam... | [
109,
2287
] | Train |
2,316 | 1 | Title: Guided Crossover: A New Operator for Genetic Algorithm Based Optimization
Abstract: Genetic algorithms (GAs) have been extensively used in different domains as a means of doing global optimization in a simple yet reliable manner. They have a much better chance of getting to global optima than gradient based me... | [
163,
743,
744,
2030,
2659
] | Validation |
2,317 | 1 | Title: Cellular Encoding Applied to Neurocontrol
Abstract: Neural networks are trained for balancing 1 and 2 poles attached to a cart on a fixed track. For one variant of the single pole system, only pole angle and cart position variables are supplied as inputs; the network must learn to compute velocities. All of th... | [
1353,
2429,
2624
] | Validation |
2,318 | 3 | Title: EXACT TRANSITION PROBABILITIES FOR THE INDEPENDENCE METROPOLIS SAMPLER
Abstract: A recent result of Jun Liu's has shown how to compute explicitly the eigen-values and eigenvectors for the Markov chain derived from a special case of the Hastings sampling algorithm, known as the indepdendence Metropolis sampler.... | [
491,
1977,
2153
] | Train |
2,319 | 0 | Title: Learning When Reformulation is Appropriate for Iterative Design
Abstract: It is well known that search-space reformulation can improve the speed and reliability of numerical optimization in engineering design. We argue that the best choice of reformulation depends on the design goal, and present a technique fo... | [
227,
2131,
2479
] | Train |
2,320 | 6 | Title: Inserting the best known bounds for weighted bipar tite matching [11], with 1=2 p polynomial-time
Abstract: we apply the reduction to their two core children, the total sum of their matching weights becomes O(n), and if for each comparison of a spine node and a critical node we apply the reduction to the core ch... | [
1853,
2511
] | Train |
2,321 | 2 | Title: Asymptotic Controllability Implies Feedback Stabilization
Abstract: | | [
2186,
2187,
2370
] | Train |
2,322 | 2 | Title: PHONETIC CLASSIFICATION OF TIMIT SEGMENTS PREPROCESSED WITH LYON'S COCHLEAR MODEL USING A SUPERVISED/UNSUPERVISED HYBRID NEURAL NETWORK
Abstract: We report results on vowel and stop consonant recognition with tokens extracted from the TIMIT database. Our current system differs from others doing similar tasks i... | [
359,
2498,
2499,
2500
] | Test |
2,323 | 3 | Title: PHONETIC CLASSIFICATION OF TIMIT SEGMENTS PREPROCESSED WITH LYON'S COCHLEAR MODEL USING A SUPERVISED/UNSUPERVISED HYBRID NEURAL NETWORK
Abstract: MOU 130: Feasibility study of fully autonomous vehicles using decision-theoretic control Final Report | [
492,
788,
1268,
2419
] | Train |
2,324 | 6 | Title: APPLICATION OF ESOP MINIMIZATION IN MACHINE LEARNING AND KNOWLEDGE DISCOVERY
Abstract: This paper presents a new application of an Exclusive-Sum-Of-Products (ESOP) minimizer EXORCISM-MV-2: to Machine Learning, and particularly, in Pattern Theory. An analysis of various logic synthesis programs has been conduct... | [
1161,
2326
] | Validation |
2,325 | 2 | Title: Incremental Polynomial Model-Controller Network: a self organising non-linear controller
Abstract: The aim of this study is to present the "Incremental Polynomial Model-Controller Network" (IPMCN). This network is composed of controllers each one attached to a model used for its indirect design. At each instan... | [
611,
745,
2538
] | Train |
2,326 | 6 | Title: Pattern Theoretic Feature Extraction and Constructive Induction
Abstract: This paper offers a perspective on features and pattern finding in general. This perspective is based on a robust complexity measure called Decomposed Function Car-dinality. A function decomposition algorithm for minimizing this complexi... | [
317,
508,
2324
] | Train |
2,327 | 6 | Title: A Comparison of New and Old Algorithms for A Mixture Estimation Problem
Abstract: We investigate the problem of estimating the proportion vector which maximizes the likelihood of a given sample for a mixture of given densities. We adapt a framework developed for supervised learning and give simple derivations ... | [
76,
1924,
2015,
2034
] | Train |
2,328 | 4 | Title: A Comparison of Direct and Model-Based Reinforcement Learning
Abstract: This paper compares direct reinforcement learning (no explicit model) and model-based reinforcement learning on a simple task: pendulum swing up. We find that in this task model-based approaches support reinforcement learning from smaller ... | [
1782
] | Validation |
2,329 | 6 | Title: Programming Research Group A LEARNABILITY MODEL FOR UNIVERSAL REPRESENTATIONS
Abstract: This paper compares direct reinforcement learning (no explicit model) and model-based reinforcement learning on a simple task: pendulum swing up. We find that in this task model-based approaches support reinforcement learni... | [
672,
1290,
1428,
1918,
2080,
2589
] | Train |
2,330 | 1 | Title: A comparison of the fixed and floating building block representation in the genetic algorithm
Abstract: This article compares the traditional, fixed problem representation style of a genetic algorithm (GA) with a new floating representation in which the building blocks of a problem are not fixed at specific lo... | [
1631,
1696,
1769,
2598,
2604
] | Validation |
2,331 | 6 | Title: Improved Hoeffding-Style Performance Guarantees for Accurate Classifiers
Abstract: We extend Hoeffding bounds to develop superior probabilistic performance guarantees for accurate classifiers. The original Hoeffding bounds on classifier accuracy depend on the accuracy itself as a parameter. Since the accuracy ... | [
571,
2694
] | Train |
2,332 | 1 | Title: Improved Hoeffding-Style Performance Guarantees for Accurate Classifiers
Abstract: Evolving Cooperative Groups: Preliminary Results Abstract Multi-agent systems require coordination of sources with distinct expertise to perform complex tasks effectively. In this paper, we use co-evolutionary approach using gen... | [
1117,
2334
] | Validation |
2,333 | 6 | Title: Recursive Automatic Algorithm Selection for Inductive Learning
Abstract: COINS Technical Report 94-61 August 1994 | [
102,
318,
2135,
2583
] | Train |
2,334 | 1 | Title: New Methods for Competitive Coevolution
Abstract: We consider "competitive coevolution," in which fitness is based on direct competition among individuals selected from two independently evolving populations of "hosts" and "parasites." Competitive coevolution can lead to an "arms race," in which the two popula... | [
54,
209,
602,
1588,
1790,
1832,
1836,
1917,
2103,
2332,
2353
] | Test |
2,335 | 2 | Title: Function Approximation with Neural Networks and Local Methods: Bias, Variance and Smoothness
Abstract: We review the use of global and local methods for estimating a function mapping R m ) R n from samples of the function containing noise. The relationship between the methods is examined and an empirical compa... | [
74,
2378
] | Test |
2,336 | 2 | Title: A Fast Kohonen Net Implementation for
Abstract: We present an implementation of Kohonen Self-Organizing Feature Maps for the Spert-II vector microprocessor system. The implementation supports arbitrary neural map topologies and arbitrary neighborhood functions. For small networks, as used in real-world tasks, ... | [
745,
2579
] | Train |
2,337 | 2 | Title: Learning to segment images using dynamic feature binding an isolated object in an image is
Abstract: Despite the fact that complex visual scenes contain multiple, overlapping objects, people perform object recognition with ease and accuracy. One operation that facilitates recognition is an early segmentation pro... | [
2218,
2606,
2610
] | Train |
2,338 | 0 | Title: Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier
Abstract: The simple Bayesian classifier (SBC) is commonly thought to assume that attributes are independent given the class, but this is apparently contradicted by the surprisingly good performance it exhibits in many domains... | [
1024,
1478,
1986,
2127,
2443,
2677
] | Test |
2,339 | 5 | Title: An intelligent search method using Inductive Logic Programming
Abstract: We propose a method to use Inductive Logic Programming to give heuristic functions for searching goals to solve problems. The method takes solutions of a problem or a history of search and a set of background knowledge on the problem. In ... | [
344,
675,
2126
] | Test |
2,340 | 2 | Title: Generalization to local remappings of the visuomotor coordinate transformation
Abstract: We propose a method to use Inductive Logic Programming to give heuristic functions for searching goals to solve problems. The method takes solutions of a problem or a history of search and a set of background knowledge on ... | [
611,
1935
] | Train |
2,341 | 3 | Title: Dynamic Belief Networks for Discrete Monitoring
Abstract: We describe the development of a monitoring system which uses sensor observation data about discrete events to construct dynamically a probabilistic model of the world. This model is a Bayesian network incorporating temporal aspects, which we call a Dyn... | [
559,
623,
788,
1172,
1757,
1842,
2425
] | Train |
2,342 | 6 | Title: The Power of Decision Tables
Abstract: We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms. Decision tables are one of the simplest hypothesis spaces possible, and usually they are easy to understand. Experimental results show that on artificial and real-world doma... | [
381,
497,
1270,
2151,
2197,
2577,
2593
] | Validation |
2,343 | 3 | Title: Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology
Abstract: In the wrapper approach to feature subset selection, a search for an optimal set of features is made using the induction algorithm as a black box. The estimated future performance of the algorithm is the ... | [
208,
430,
1337,
1618,
2443
] | Train |
2,344 | 2 | Title: A Neural Network Based Head Tracking System
Abstract: We have constructed an inexpensive, video-based, motorized tracking system that learns to track a head. It uses real time graphical user inputs or an auxiliary infrared detector as supervisory signals to train a convolutional neural network. The inputs to t... | [
2707
] | Test |
2,345 | 6 | Title: The Hardness of Problems on Thin Colored Graphs
Abstract: In this paper, we consider the complexity of a number of combinatorial problems; namely, Intervalizing Colored Graphs (DNA physical mapping), Triangulating Colored Graphs (perfect phylogeny), (Directed) (Modified) Colored Cutwidth, Feasible Register Ass... | [
2005,
2418,
2511
] | Train |
2,346 | 6 | Title: Parity: The Problem that Won't Go Away
Abstract: It is well-known that certain learning methods (e.g., the perceptron learning algorithm) cannot acquire complete, parity mappings. But it is often overlooked that state-of-the-art learning methods such as C4.5 and backpropagation cannot generalise from incomplet... | [
397,
1301,
1595,
1967
] | Train |
2,347 | 1 | Title: Chapter 4 Empirical comparison of stochastic algorithms Empirical comparison of stochastic algorithms in a graph
Abstract: There are several stochastic methods that can be used for solving NP-hard optimization problems approximatively. Examples of such algorithms include (in order of increasing computational com... | [
1303,
2254,
2265
] | Train |
2,348 | 3 | Title: Sequential Importance Sampling for Nonparametric Bayes Models: The Next Generation Running Title: SIS for Nonparametric Bayes
Abstract: There are two generations of Gibbs sampling methods for semi-parametric models involving the Dirichlet process. The first generation suffered from a severe drawback; namely th... | [
1783,
2682
] | Train |
2,349 | 2 | Title: No Free Lunch for Early Stopping
Abstract: We show that, with a uniform prior on hypothesis functions having the same training error, early stopping at some fixed training error above the training error minimum results in an increase in the expected generalization error. We also show that regularization method... | [
1929
] | Train |
2,350 | 6 | Title: Exact Learning of -DNF Formulas with Malicious Membership Queries
Abstract: We show that, with a uniform prior on hypothesis functions having the same training error, early stopping at some fixed training error above the training error minimum results in an increase in the expected generalization error. We als... | [
1004,
2168
] | Train |
2,351 | 2 | Title: ERROR STABILITY PROPERTIES OF GENERALIZED GRADIENT-TYPE ALGORITHMS
Abstract: We present a unified framework for convergence analysis of the generalized subgradient-type algorithms in the presence of perturbations. One of the principal novel features of our analysis is that perturbations need not tend to zero i... | [
1772
] | Train |
2,352 | 2 | Title: Power System Security Margin Prediction Using Radial Basis Function Networks
Abstract: Dr. McCalley's research is partially supported through grants from National Science Foundation and Pacific Gas and Electric Company. Dr. Honavar's research is partially supported through grants from National Science Foundati... | [
611,
2055
] | Validation |
2,353 | 1 | Title: Dynamics of Co-evolutionary Learning
Abstract: Co-evolutionary learning, which involves the embedding of adaptive learning agents in a fitness environment which dynamically responds to their progress, is a potential solution for many technological chicken and egg problems, and is at the heart of several recent... | [
415,
2334
] | Test |
2,354 | 6 | Title: The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs
Abstract: We show that two cooperating robots can learn exactly any strongly-connected directed graph with n indistinguishable nodes in expected time polynomial in n. We introduce a new type of homing sequence for two robots, which h... | [
400,
453,
555,
556,
2360,
2455
] | Train |
2,355 | 2 | Title: CONVIS: Action Oriented Control and Visualization of Neural Networks Introduction and Technical Description
Abstract: We show that two cooperating robots can learn exactly any strongly-connected directed graph with n indistinguishable nodes in expected time polynomial in n. We introduce a new type of homing se... | [
217,
763,
1879
] | Train |
2,356 | 6 | Title: Learning With Unreliable Boundary Queries
Abstract: We introduce a model for learning from examples and membership queries in situations where the boundary between positive and negative examples is somewhat ill-defined. In our model, queries near the boundary of a target concept may receive incorrect or "don't... | [
1105,
1705,
2246
] | Train |
2,357 | 2 | Keyword: Running Title: Local Multivariate Binary Processors
Abstract: We thank Sue Becker, Peter Hancock and Darragh Smyth for helpful comments on this work. The work of Dario Floreano and Bill Phillips was supported by a Network Grant from the Human Capital and Mobility Programme of the European Community. | [
1656,
2499
] | Train |
2,358 | 2 | Title: Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular
Abstract: To understand the interspike interval (ISI) variability displayed by visual cortical neurons (Softky and Koch, 1993), it is critical to examine the dynamics of their neuronal integration as well as the variability ... | [
2503
] | Train |
2,359 | 0 | Title: Computer-Supported Argumentation for Cooperative Design on the World-Wide Web
Abstract: This paper describes an argumentation system for cooperative design applications on the Web. The system provides experts involved in such procedures means of expressing and weighing their individual arguments and preference... | [
66,
2520
] | Train |
2,360 | 6 | Title: Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)
Abstract: This paper describes new and efficient algorithms for learning deterministic finite automata. Our approach is primarily distinguished by two features: (1) the adoption of an average-case setting to model the "typical"... | [
400,
556,
574,
672,
1006,
1386,
2004,
2040,
2273,
2354
] | Train |
2,361 | 1 | Title: Program Search with a Hierarchical Variable Length Representation: Genetic Programming, Simulated Annealing and Hill Climbing
Abstract: This paper presents a comparison of Genetic Programming(GP) with Simulated Annealing (SA) and Stochastic Iterated Hill Climbing (SIHC) based on a suite of program discovery pr... | [
139,
163,
2175,
2216
] | Train |
2,362 | 3 | Title: Outperforming the Gibbs sampler empirical estimator for nearest neighbor random fields
Abstract: Given a Markov chain sampling scheme, does the standard empirical estimator make best use of the data? We show that this is not so and construct better estimators. We restrict attention to nearest neighbor random f... | [
1870,
2510
] | Test |
2,363 | 1 | Title: Modeling the Evolution of Motivation
Abstract: In order for learning to improve the adaptiveness of an animal's behavior and thus direct evolution in the way Baldwin suggested, the learning mechanism must incorporate an innate evaluation of how the animal's actions influence its reproductive fitness. For examp... | [
129,
163,
1719,
1969,
2165
] | Test |
2,364 | 0 | Title: Automatic Phonetic Transcription of Words Based On Sparse Data
Abstract: The relation between the orthography and the phonology of a language has traditionally been modelled by hand-crafted rule sets. Machine-learning (ML) approaches offer a means to gather this knowledge automatically. Problems arise when the... | [
862,
1644,
1812
] | Train |
2,365 | 5 | Title: A Reduced Multipipeline Machine Description that Preserves Scheduling Constraints
Abstract: High performance compilers increasingly rely on accurate modeling of the machine resources to efficiently exploit the instruction level parallelism of an application. In this paper, we propose a reduced machine descript... | [
2189,
2190,
2668
] | Train |
2,366 | 3 | Title: Choice of Thresholds for Wavelet Shrinkage Estimate of the Spectrum fff j g are level-dependent
Abstract: We study the problem of estimating the log spectrum of a stationary Gaussian time series by thresholding the empirical wavelet coefficients. We propose the use of thresholds t j;n depending on sample size n,... | [
1910,
2081,
2458,
2506,
2575
] | Train |
2,367 | 6 | Title: Data Mining using MLC A Machine Learning Library in C http://www.sgi.com/Technology/mlc
Abstract: Data mining algorithms including machine learning, statistical analysis, and pattern recognition techniques can greatly improve our understanding of data warehouses that are now becoming more widespread. In this p... | [
1833,
2577
] | Train |
2,368 | 4 | Title: Reinforcement Learning with Modular Neural Networks for Control
Abstract: Reinforcement learning methods can be applied to control problems with the objective of optimizing the value of a function over time. They have been used to train single neural networks that learn solutions to whole tasks. Jacobs and Jor... | [
85,
465,
2642
] | Train |
2,369 | 0 | Title: Case-Based Sonogram Classification
Abstract: This report replicates and extends results reported by Naval Air Warfare Center (NAWC) personnel on the automatic classification of sonar images. They used novel case-based reasoning systems in their empirical studies, but did not obtain comparative analyses using s... | [
256,
426,
2607
] | Validation |
2,370 | 2 | Title: CONTROL-LYAPUNOV FUNCTIONS FOR TIME-VARYING SET STABILIZATION
Abstract: This paper shows that, for time varying systems, global asymptotic controllability to a given closed subset of the state space is equivalent to the existence of a continuous control-Lyapunov function with respect to the set. | [
2321
] | Train |
2,371 | 0 | Title: Learning Adaptation Strategies by Introspective Reasoning about Memory Search
Abstract: In case-based reasoning systems, the case adaptation process is traditionally controlled by static libraries of hand-coded adaptation rules. This paper proposes a method for learning adaptation knowledge in the form of adap... | [
583,
1126,
2489
] | Test |
2,372 | 0 | Title: Goal-Driven Learning: Fundamental Issues (A Symposium Report)
Abstract: In Artificial Intelligence, Psychology, and Education, a growing body of research supports the view that learning is a goal-directed process. Psychological experiments show that people with different goals process information differently; ... | [
1126,
2398,
2489
] | Train |
2,373 | 2 | Title: Evaluating Neural Network Predictors by Bootstrapping
Abstract: We present a new method, inspired by the bootstrap, whose goal it is to determine the quality and reliability of a neural network predictor. Our method leads to more robust forecasting along with a large amount of statistical information on foreca... | [
916,
1366,
2374,
2413,
2414,
2507
] | Train |
2,374 | 2 | Title: Predictions with Confidence Intervals (Local Error Bars)
Abstract: We present a new method for obtaining local error bars, i.e., estimates of the confidence in the predicted value that depend on the input. We approach this problem of nonlinear regression in a maximum likelihood framework. We demonstrate our te... | [
916,
1366,
2373,
2507,
2513
] | Validation |
2,375 | 3 | Title: Minimax Bayes, asymptotic minimax and sparse wavelet priors
Abstract: Pinsker(1980) gave a precise asymptotic evaluation of the minimax mean squared error of estimation of a signal in Gaussian noise when the signal is known a priori to lie in a compact ellipsoid in Hilbert space. This `Minimax Bayes' method ca... | [
1910,
2416,
2661
] | Train |
2,376 | 2 | Title: Multimodality Exploration in Training an Unsupervised Projection Pursuit Neural Network
Abstract: Graphical inspection of multimodality is demonstrated using unsupervised lateral-inhibition neural networks. Three projection pursuit indices are compared on low dimensional simulated and real-world data: principa... | [
359,
2422,
2499,
2500
] | Train |
2,377 | 3 | Title: Adaptive proposal distribution for random walk Metropolis algorithm
Abstract: The choice of a suitable MCMC method and further the choice of a proposal distribution is known to be crucial for the convergence of the Markov chain. However, in many cases the choice of an effective proposal distribution is difficu... | [
468,
491,
2025
] | Train |
2,378 | 2 | Title: Priors, Stabilizers and Basis Functions: from regularization to radial, tensor and additive splines
Abstract: We had previously shown that regularization principles lead to approximation schemes which are equivalent to networks with one layer of hidden units, called Regularization Networks. In particular we ha... | [
611,
1668,
2050,
2335
] | Validation |
2,379 | 1 | Title: The Evolution of Size in Variable Length Representations
Abstract: In many cases programs length's increase (known as bloat, fluff and increasing structural complexity) during artificial evolution. We show bloat is not specific to genetic programming and suggest it is inherent in search techniques with discret... | [
1984,
2206
] | Validation |
2,380 | 3 | Title: Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics
Abstract: We propose a probabilistic case-space metric for the case matching and case adaptation tasks. Central to our approach is a probability propagation algorithm adopted from Bayesian reasoning systems, which allows our case-bas... | [
215,
288,
485,
1838,
2294,
2514,
2561
] | Train |
2,381 | 2 | Title: Evolving Artificial Neural Networks using the Baldwin Effect
Abstract: This paper describes how through simple means a genetic search towards optimal neural network architectures can be improved, both in the convergence speed as in the quality of the final result. This result can be theoretically explained wit... | [
687,
1606,
2667
] | Train |
2,382 | 2 | Title: Trees and Splines in Survival Analysis
Abstract: Technical Report No. 275 Revised March 30, 1995 University of Washington Department of Statistics Seattle, Washington 98195 Abstract During the past few years several nonparametric alternatives to the Cox proportional hazards model have appeared in the literatur... | [
2013
] | Train |
2,383 | 2 | Title: Alternative Discrete-Time Operators and Their Application to Nonlinear Models
Abstract: Technical Report No. 275 Revised March 30, 1995 University of Washington Department of Statistics Seattle, Washington 98195 Abstract During the past few years several nonparametric alternatives to the Cox proportional hazar... | [
427,
1820
] | Test |
2,384 | 3 | Title: ESTIMATING FUNCTIONS OF PROBABILITY DISTRIBUTIONS FROM A FINITE SET OF SAMPLES Part II: Bayes Estimators
Abstract: This paper is the second in a series of two on the problem of estimating a function of a probability distribution from a finite set of samples of that distribution. In the first paper 1 , the Bayes ... | [
2460
] | Train |
2,385 | 2 | Title: Receptive Fields for Vision: from Hyperacuity to Object Recognition
Abstract: Many of the lower-level areas in the mammalian visual system are organized retinotopically, that is, as maps which preserve to a certain degree the topography of the retina. A unit that is a part of such a retinotopic map normally re... | [
611,
2499,
2676
] | Train |
2,386 | 2 | Title: Optimising Local Hebbian Learning: use the ffi-rule
Abstract: Many of the lower-level areas in the mammalian visual system are organized retinotopically, that is, as maps which preserve to a certain degree the topography of the retina. A unit that is a part of such a retinotopic map normally responds selective... | [
2387
] | Validation |
2,387 | 2 | Title: CNN: a Neural Architecture that Learns Multiple Transformations of Spatial Representations
Abstract: Many of the lower-level areas in the mammalian visual system are organized retinotopically, that is, as maps which preserve to a certain degree the topography of the retina. A unit that is a part of such a reti... | [
2386
] | Test |
2,388 | 2 | Title: Combining Neural Network Forecasts on Wavelet-Transformed Time Series
Abstract: Many of the lower-level areas in the mammalian visual system are organized retinotopically, that is, as maps which preserve to a certain degree the topography of the retina. A unit that is a part of such a retinotopic map normally ... | [
427,
2575
] | Test |
2,389 | 3 | Title: On Computing the Largest Fraction of Missing Information for the EM Algorithm and the Worst
Abstract: We address the problem of computing the largest fraction of missing information for the EM algorithm and the worst linear function for data augmentation. These are the largest eigenvalue and its associated eigen... | [
345,
2421
] | Test |
2,390 | 2 | Title: A HIERARCHICAL COMMUNITY OF EXPERTS
Abstract: We describe a directed acyclic graphical model that contains a hierarchy of linear units and a mechanism for dynamically selecting an appropriate subset of these units to model each observation. The non-linear selection mechanism is a hierarchy of binary units each... | [
36,
74,
76,
2227
] | Validation |
2,391 | 6 | Title: A Note on Learning from Multiple-Instance Examples
Abstract: We describe a simple reduction from the problem of PAC-learning from multiple-instance examples to that of PAC-learning with one-sided random classification noise. Thus, all concept classes learnable with one-sided noise, which includes all concepts ... | [
507,
2427,
2548
] | Validation |
2,392 | 1 | Title: Hierarchical Learning with Procedural Abstraction Mechanisms
Abstract: We describe a simple reduction from the problem of PAC-learning from multiple-instance examples to that of PAC-learning with one-sided random classification noise. Thus, all concept classes learnable with one-sided noise, which includes all... | [
1925
] | Train |
2,393 | 2 | Title: Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN)
Abstract: The absence of powerful control structures and processes that synchronize, coordinate, switch between, choose among, regulate, direct, modulate interactions between, and combine distinct yet interdependen... | [
496,
663,
1813,
1896,
1952,
2029
] | Train |
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