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train · 203k rows

node_id int64 0 76.9k	label int64 0 39	text stringlengths 13 124k	neighbors listlengths 0 3.32k	mask stringclasses 4 values
894	3	Title: Bayesian Analysis of Agricultural Field Experiments Abstract: SUMMARY The paper describes Bayesian analysis for agricultural field experiments, a topic that has received very little previous attention, despite a vast frequentist literature. Adoption of the Bayesian paradigm simplifies the interpretation of the...	[ 1255 ]	Test
895	3	Title: CABeN: A Collection of Algorithms for Belief Networks Correspond with: Abstract: Portions of this report have been published in the Proceedings of the Fifteenth Annual Symposium on Computer Applications in Medical Care (November, 1991).	[ 1436 ]	Train
896	3	Title: Discretization of continuous Markov chains and MCMC convergence assessment Abstract: We show in this paper that continuous state space Markov chains can be rigorously discretized into finite Markov chains. The idea is to subsample the continuous chain at renewal times related to small sets which control the di...	[ 468, 888, 1372 ]	Train
897	3	Title: Parallel Markov chain Monte Carlo sampling. Abstract: Markov chain Monte Carlo (MCMC) samplers have proved remarkably popular as tools for Bayesian computation. However, problems can arise in their application when the density of interest is high dimensional and strongly correlated. In these circumstances the ...	[ 157, 1240 ]	Train
898	3	Title: database Abstract: MIT Computational Cognitive Science Technical Report 9701 Abstract We describe variational approximation methods for efficient probabilistic reasoning, applying these methods to the problem of diagnostic inference in the QMR-DT database. The QMR-DT database is a large-scale belief network ba...	[ 107, 108, 1687 ]	Train
899	2	Title: GA-RBF: A Self-Optimising RBF Network Abstract: The effects of a neural network's topology on its performance are well known, yet the question of finding optimal configurations automatically remains largely open. This paper proposes a solution to this problem for RBF networks. A self- optimising approach, driv...	[ 611, 1564, 1565, 1672 ]	Train
900	1	Title: Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect Using Learning to Facilitate the Abstract: This paper describes a hybrid methodology that integrates genetic algorithms and decision tree learning in order to evolve useful subsets of discriminatory features for recognizing complex visual concept...	[ 228, 1207, 1351, 1533, 1583 ]	Train
901	0	Title: Evaluating Computational Assistance for Crisis Response Abstract: In this paper we examine the behavior of a human-computer system for crisis response. As one instance of crisis management, we describe the task of responding to spills and fires involving hazardous materials. We then describe INCA, an intellige...	[ 1497, 1553, 1554 ]	Validation
902	1	Title: Explanations of Empirically Derived Reactive Plans Abstract: Given an adequate simulation model of the task environment and payoff function that measures the quality of partially successful plans, competition-based heuristics such as genetic algorithms can develop high performance reactive rules for interestin...	[ 910, 964, 965, 966, 981, 1174 ]	Test
903	6	Title: Learning Concepts from Sensor Data of a Mobile Robot Abstract: Machine learning can be a most valuable tool for improving the flexibility and efficiency of robot applications. Many approaches to applying machine learning to robotics are known. Some approaches enhance the robot's high-level processing, the plan...	[ 1491, 1672 ]	Test
904	3	Title: Assessing Convergence of Markov Chain Monte Carlo Algorithms Abstract: We motivate the use of convergence diagnostic techniques for Markov Chain Monte Carlo algorithms and review various methods proposed in the MCMC literature. A common notation is established and each method is discussed with particular empha...	[ 115, 292, 352, 889, 892, 1372, 1733 ]	Validation
905	3	Title: Compositional Modeling With DPNs Abstract: We motivate the use of convergence diagnostic techniques for Markov Chain Monte Carlo algorithms and review various methods proposed in the MCMC literature. A common notation is established and each method is discussed with particular emphasis on implementational issu...	[ 558, 976, 1287, 1393, 1397 ]	Train
906	2	Title: Memory-based Time Series Recognition A New Methodology and Real World Applications Abstract: We motivate the use of convergence diagnostic techniques for Markov Chain Monte Carlo algorithms and review various methods proposed in the MCMC literature. A common notation is established and each method is discusse...	[ 74, 1019, 1860 ]	Train
907	2	Title: Visual Tracking of Moving Objects using a Neural Network Controller Abstract: For a target tracking task, the hand-held camera of the anthropomorphic OSCAR-robot manipulator has to track an object which moves arbitrarily on a table. The desired camera-joint mapping is approximated by a feedforward neural netwo...	[ 1252 ]	Validation
908	2	Title: Eclectic Machine Learning Abstract: For a target tracking task, the hand-held camera of the anthropomorphic OSCAR-robot manipulator has to track an object which moves arbitrarily on a table. The desired camera-joint mapping is approximated by a feedforward neural network. Through the use of time derivatives of...	[ 414, 809, 919, 2304 ]	Test
909	3	Title: Regression Can Build Predictive Causal Models Abstract: Covariance information can help an algorithm search for predictive causal models and estimate the strengths of causal relationships. This information should not be discarded after conditional independence constraints are identified, as is usual in contemp...	[ 827, 913, 1527, 1894 ]	Train
910	1	Title: Learning Sequential Decision Rules Using Simulation Models and Competition Abstract: The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical decision rules from a simple flight simulator. The learning method relies on the notion of competition and ...	[ 163, 523, 565, 764, 811, 902, 964, 966, 981, 982, 1117, 1131, 1140, 1221, 1253, 1311, 1432, 1481, 1573, 1589, 1590, 1673 ]	Train
911	6	Title: Utilizing Prior Concepts for Learning Abstract: The inductive learning problem consists of learning a concept given examples and non-examples of the concept. To perform this learning task, inductive learning algorithms bias their learning method. Here we discuss biasing the learning method to use previously le...	[ 177, 585, 1354 ]	Validation
912	2	Title: Statistical Ideas for Selecting Network Architectures Abstract: Choosing the architecture of a neural network is one of the most important problems in making neural networks practically useful, but accounts of applications usually sweep these details under the carpet. How many hidden units are needed? Should w...	[ 1149, 1150, 1240, 1241 ]	Validation
913	3	Title: A Statistical Semantics for Causation Key words: causality, induction, learning Abstract: We propose a model-theoretic definition of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious covari-ations following standard norms of inductive reasoning....	[ 827, 909 ]	Train
914	2	Title: All-to-all Broadcast on the CNS-1 Abstract: This study deals with the all-to-all broadcast on the CNS-1. We determine a lower bound for the run time and present an algorithm meeting this bound. Since this study points out a bottleneck in the network interface, we also analyze the performance of alternative int...	[ 272, 1551 ]	Train
915	3	Title: Abstract Abstract: Automated decision making is often complicated by the complexity of the knowledge involved. Much of this complexity arises from the context-sensitive variations of the underlying phenomena. We propose a framework for representing descriptive, context-sensitive knowledge. Our approach attempt...	[ 1172, 1294 ]	Validation
916	2	Title: A comparison of some error estimates for neural network models Summary Abstract: We discuss a number of methods for estimating the standard error of predicted values from a multi-layer perceptron. These methods include the delta method based on the Hessian, bootstrap estimators, and the "sandwich" estimator. ...	[ 157, 427, 1038, 2373, 2374, 2686 ]	Train
917	3	Title: Practical Bayesian Inference Using Mixtures of Mixtures Abstract: Discrete mixtures of normal distributions are widely used in modeling amplitude fluctuations of electrical potentials at synapses of human, and other animal nervous systems. The usual framework has independent data values y j arising as y j = j ...	[ 784, 850, 1338 ]	Train
918	2	Title: Predictive Robot Control with Neural Networks Abstract: Neural controllers are able to position the hand-held camera of the (3DOF) anthropomorphic OSCAR-robot manipulator above an object which is arbitrary placed on a table. The desired camera-joint mapping is approximated by feedforward neural networks. Howev...	[ 1252 ]	Train
919	2	Title: A Generalizing Adaptive Discriminant Network Abstract: This paper overviews the AA1 (Adaptive Algorithm 1) model of ASOCS the (Adaptive Self - Organizing Concurrent Systems) approach. It also presents promising empirical generalization results of AA1 with actual data. AA1 is a topologically dynamic network whi...	[ 809, 908, 1129 ]	Validation
920	2	Title: Maximum likelihood source separation for discrete sources Abstract: This communication deals with the source separation problem which consists in the separation of a noisy mixture of independent sources without a priori knowledge of the mixture coefficients. In this paper, we consider the maximum likelihood (M...	[ 873, 1520 ]	Validation
921	2	Title: DIAGNOSING AND CORRECTING SYSTEM ANOMALIES WITH A ROBUST CLASSIFIER Abstract: If a robust statistical model has been developed to classify the ``health'' of a system, a well-known Taylor series approximation technique forms the basis of a diagnostic/recovery procedure that can be initiated when the system's he...	[ 1265, 1725 ]	Train
922	0	Title: Towards Improving Case Adaptability with a Genetic Algorithm Abstract: Case combination is a difficult problem in Case Based Reasoning, as sub-cases often exhibit conflicts when merged together. In our previous work we formalized case combination by representing each case as a constraint satisfaction problem, ...	[ 580, 923, 1233, 1416 ]	Validation
923	0	Title: Dynamic Constraint Satisfaction using Case-Based Reasoning Techniques Abstract: The Dynamic Constraint Satisfaction Problem (DCSP) formalism has been gaining attention as a valuable and often necessary extension of the static CSP framework. Dynamic Constraint Satisfaction enables CSP techniques to be applied m...	[ 922, 1126, 1233, 1416 ]	Train
924	6	Title: Quantifying Prior Determination Knowledge using the PAC Learning Model Abstract: Prior knowledge, or bias, regarding a concept can speed up the task of learning it. Probably Approximately Correct (PAC) learning is a mathematical model of concept learning that can be used to quantify the speed up due to differe...	[ 672, 1309, 1460, 1480, 1539 ]	Train
925	2	Title: Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration Abstract: Computational models of natural systems often contain free parameters that must be set to optimize the predictive accuracy of the models. This process\| called calibration\|can be viewed as a form of supervised learning i...	[ 1532 ]	Test
926	6	Title: Virtual Seens and the Frequently Used Dataset Abstract: The paper considers the situation in which a learner's testing set contains close approximations of cases which appear in the training set. Such cases can be considered `virtual seens' since they are approximately seen by the learner. Generalisation measu...	[ 1019, 1112 ]	Train
927	0	Title: Exemplar-based Music Structure Recognition Abstract: We tend to think of what we really know as what we can talk about, and disparage knowledge that we can't verbalize. [Dowling 1989, p. 252]	[ 1019, 1328 ]	Train
928	0	Title: Learning to Refine Case Libraries: Abstract: Initial Results Abstract. Conversational case-based reasoning (CBR) systems, which incrementally extract a query description through a user-directed conversation, are advertised for their ease of use. However, designing large case libraries that have good performanc...	[ 256, 983, 1636 ]	Test
929	3	Title: In: A Mixture Model System for Medical and Machine Diagnosis Abstract: Diagnosis of human disease or machine fault is a missing data problem since many variables are initially unknown. Additional information needs to be obtained. The joint probability distribution of the data can be used to solve this problem...	[ 71, 740, 1559, 1697, 2442 ]	Test
930	2	Title: BACKPROPAGATION CAN GIVE RISE TO SPURIOUS LOCAL MINIMA EVEN FOR NETWORKS WITHOUT HIDDEN LAYERS Abstract: We give an example of a neural net without hidden layers and with a sigmoid transfer function, together with a training set of binary vectors, for which the sum of the squared errors, regarded as a function...	[ 805, 1062, 1254 ]	Train
931	6	Title: MAJORITY VOTE CLASSIFIERS: THEORY AND APPLICATIONS Abstract: We give an example of a neural net without hidden layers and with a sigmoid transfer function, together with a training set of binary vectors, for which the sum of the squared errors, regarded as a function of the weights, has a local minimum which i...	[ 70, 1000, 1053, 1463, 1484 ]	Test
932	6	Title: Learning an Optimally Accurate Representational System Abstract: The multiple extension problem arises because a default theory can use different subsets of its defaults to propose different, mutually incompatible, answers to some queries. This paper presents an algorithm that uses a set of observations to lea...	[ 251, 865, 1505 ]	Test
933	4	Title: Learning an Optimally Accurate Representational System Abstract: Multigrid Q-Learning Charles W. Anderson and Stewart G. Crawford-Hines Technical Report CS-94-121 October 11, 1994	[ 483, 875 ]	Train
934	1	Title: Complexity Compression and Evolution Abstract: Compression of information is an important concept in the theory of learning. We argue for the hypothesis that there is an inherent compression pressure towards short, elegant and general solutions in a genetic programming system and other variable length evolutio...	[ 380, 844, 860, 940, 1009, 1353, 1631 ]	Validation
935	1	Title: Complexity Compression and Evolution Abstract: CBR Assisted Explanation of GA Results Computer Science Technical Report number 361 CRCC Technical Report number 63	[ 163, 1136 ]	Train
936	4	Title: XCS Classifier System Reliably Evolves Accurate, Complete, and Minimal Representations for Boolean Functions more complex Abstract: Wilson's recent XCS classifier system forms complete mappings of the payoff environment in the reinforcement learning tradition thanks to its accuracy based fitness. According to Wi...	[ 1515 ]	Train
937	0	Title: The RISE System: Conquering Without Separating Abstract: Current rule induction systems (e.g. CN2) typically rely on a "separate and conquer" strategy, learning each rule only from still-uncovered examples. This results in a dwindling number of examples being available for learning successive rules, adversely ...	[ 426, 1234 ]	Train
938	1	Title: Genetic Programming of Minimal Neural Nets Using Occam's Razor Abstract: A genetic programming method is investigated for optimizing both the architecture and the connection weights of multilayer feedforward neural networks. The genotype of each network is represented as a tree whose depth and width are dynami...	[ 560, 611, 1127, 2196 ]	Train
939	2	Title: A Simple Neural Network Models Categorical Perception of Facial Expressions Abstract: The performance of a neural network that categorizes facial expressions is compared with human subjects over a set of experiments using interpolated imagery. The experiments for both the human subjects and neural networks mak...	[ 1242 ]	Train
940	1	Title: Signal Path Oriented Approach for Generation of Dynamic Process Models Abstract: The article at hand discusses a tool for automatic generation of structured models for complex dynamic processes by means of genetic programming. In contrast to other techniques which use genetic programming to find an appropriate...	[ 163, 934 ]	Train
941	1	Title: Hyperplane Ranking in Simple Genetic Algorithms Abstract: We examine the role of hyperplane ranking during search performed by a simple genetic algorithm. We also develop a metric for measuring the degree of ranking that exists with respect to static measurements taken directly from the function, as well as th...	[ 1441, 1717 ]	Test
942	1	Title: Genetic Programming Methodology, Parallelization and Applications par Abstract: We examine the role of hyperplane ranking during search performed by a simple genetic algorithm. We also develop a metric for measuring the degree of ranking that exists with respect to static measurements taken directly from the ...	[ 163, 1153 ]	Train
943	1	Title: Crossover or Mutation? Abstract: Genetic algorithms rely on two genetic operators crossover and mutation. Although there exists a large body of conventional wisdom concerning the roles of crossover and mutation, these roles have not been captured in a theoretical fashion. For example, it has never been theoret...	[ 728, 793, 1016, 1466, 1646 ]	Validation
944	3	Title: Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting Abstract: In a recent paper, Friedman, Geiger, and Goldszmidt [8] introduced a classifier based on Bayesian networks, called Tree Augmented Naive Bayes (TAN), that outperforms naive Bayes ...	[ 1335, 1337 ]	Train
945	3	Title: Structured Representation of Complex Stochastic Systems Abstract: This paper considers the problem of representing complex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic processes. Unfortunately, they are often unwieldy since they cannot ...	[ 62, 327, 788, 1287, 1414 ]	Test
946	2	Title: Constructive Learning of Recurrent Neural Networks: Limitations of Recurrent Casade Correlation and a Simple Solution Abstract: It is often difficult to predict the optimal neural network size for a particular application. Constructive or destructive methods that add or subtract neurons, layers, connections, e...	[ 427, 1179, 1600 ]	Train
947	0	Title: An Optimal Weighting Criterion of Case Indexing for Both Numeric and Symbolic Attributes Abstract: Indexing of cases is an important topic for Memory-Based Reasoning(MBR). One key problem is how to assign weights to attributes of cases. Although several weighting methods have been proposed, some methods cannot...	[ 1328 ]	Validation
948	2	Title: An Optimal Weighting Criterion of Case Indexing for Both Numeric and Symbolic Attributes Abstract: A General Result on the Stabilization of Linear Systems Using Bounded Controls 1 ABSTRACT We present two constructions of controllers that globally stabilize linear systems subject to control saturation. We allow...	[ 1281, 1282, 1446 ]	Train
949	2	Title: Classifying Seismic Signals by Integrating Ensembles of Neural Networks Abstract: This paper proposes a classification scheme based on integration of multiple Ensembles of ANNs. It is demonstrated on a classification problem, in which seismic signals of Natural Earthquakes must be distinguished from seismic si...	[ 74, 1512, 1608 ]	Train
950	3	Title: Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology 1 Abstract: 1 This is the first draft of a chapter for Bayesian Biostatistics, edited by Donald A. Berry and Darlene K. Strangl. Adrian E. Raftery is Professor of Statistics and Sociology, Department of Statistics, GN-22, ...	[ 84, 1240, 1241 ]	Test
951	0	Title: Using Case-Based Reasoning to Acquire User Scheduling Preferences that Change over Time Abstract: Production/Manufacturing scheduling typically involves the acquisition of user optimization preferences. The ill-structuredness of both the problem space and the desired objectives make practical scheduling proble...	[ 1401, 1554, 2605 ]	Train
952	3	Title: Some Varieties of Qualitative Probability Abstract:	[ 1064, 1660 ]	Test
953	1	Title: Behavior Hierarchy for Autonomous Mobile Robots: Fuzzy-behavior modulation and evolution Abstract: Realization of autonomous behavior in mobile robots, using fuzzy logic control, requires formulation of rules which are collectively responsible for necessary levels of intelligence. Such a collection of rules ca...	[ 972 ]	Train
954	3	Title: Unsupervised learning of distributions on binary vectors using two layer networks Abstract: We present a distribution model for binary vectors, called the influence combination model and show how this model can be used as the basis for unsupervised learning algorithms for feature selection. The model is closel...	[ 427, 450, 969, 1461, 1591 ]	Test
955	6	Title: Separating Formal Bounds from Practical Performance in Learning Systems Abstract: We present a distribution model for binary vectors, called the influence combination model and show how this model can be used as the basis for unsupervised learning algorithms for feature selection. The model is closely related ...	[ 109, 560, 1280 ]	Train
956	1	Title: Modeling Distributed Search via Social Insects Abstract: Complex group behavior arises in social insects colonies as the integration of the actions of simple and redundant individual insects [Adler and Gordon, 1992, Oster and Wilson, 1978]. Furthermore, the colony can act as an information center to expedite f...	[ 995, 1178, 1231, 2598 ]	Test
957	6	Title: ANNEALED THEORIES OF LEARNING Abstract: We study annealed theories of learning boolean functions using a concept class of finite cardinality. The naive annealed theory can be used to derive a universal learning curve bound for zero temperature learning, similar to the inverse square root bound from the Vapnik-...	[ 967 ]	Train
958	1	Title: The Evolution of Memory and Mental Models Using Genetic Programming build internal representations of their Abstract: This paper applies genetic programming their successive actions. The results show to the evolution of intelligent agents that	[ 1409 ]	Train
959	1	Title: Numerical techniques for efficient sonar bearing and range searching in the near field using genetic algorithms Abstract: This article describes a numerical method that may be used to efficiently locate and track underwater sonar targets in the near-field, with both bearing and range estimation, for the case o...	[ 793, 1130 ]	Validation
960	5	Title: Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Constructive Induction-based Learning Agents: Abstract: This paper introduces a new type of intelligent agent called a constructive induction-based learning agent (CILA). This agent differs from other adaptive agents because...	[ 378, 1292, 1301 ]	Train
961	1	Title: CFS-C: A Package of Domain Independent Subroutines for Implementing Classifier Systems in Arbitrary, User-Defined Environments. Abstract: This paper introduces a new type of intelligent agent called a constructive induction-based learning agent (CILA). This agent differs from other adaptive agents because it h...	[ 163, 523, 1515 ]	Train
962	2	Title: Using Many-Particle Decomposition to get a Parallel Self-Organising Map Abstract: We propose a method for decreasing the computational complexity of self-organising maps. The method uses a partitioning of the neurons into disjoint clusters. Teaching of the neurons occurs on a cluster-basis instead of on a neur...	[ 745, 829 ]	Train
963	5	Title: Cooperation of Data-driven and Model-based Induction Methods for Relational Learning Abstract: Inductive learning in relational domains has been shown to be intractable in general. Many approaches to this task have been suggested nevertheless; all in some way restrict the hypothesis space searched. They can be...	[ 344, 1519 ]	Train
964	1	Title: Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment Abstract: The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical plans from a simple flight simulator where a plane must avoid ...	[ 902, 910, 965, 966, 1140, 1432, 1481, 1673 ]	Train
965	1	Title: Improving Tactical Plans with Genetic Algorithms Abstract:	[ 177, 902, 964, 966, 981, 1060, 1140, 1253, 1432, 1481, 1590, 1673 ]	Train
966	1	Title: Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation Abstract: Navigation through obstacles such as mine fields is an important capability for autonomous underwater vehicles. One way to produce robust behavior is to perform projective planning. However, real-time performan...	[ 163, 902, 910, 964, 965, 1131, 1140, 1481, 1673 ]	Train
967	6	Title: Rigorous Learning Curve Bounds from Statistical Mechanics Abstract: In this paper we introduce and investigate a mathematically rigorous theory of learning curves that is based on ideas from statistical mechanics. The advantage of our theory over the well-established Vapnik-Chervonenkis theory is that our boun...	[ 56, 57, 109, 306, 322, 689, 847, 848, 957, 1032, 1394, 1400, 1561 ]	Test
968	2	Title: Using Prior Knowledge in an NNPDA to Learn Context-Free Languages Abstract: Although considerable interest has been shown in language inference and automata induction using recurrent neural networks, success of these models has mostly been limited to regular languages. We have previously demonstrated that Neur...	[ 1285 ]	Validation
969	3	Title: Learning Stochastic Feedforward Networks Abstract: Connectionist learning procedures are presented for "sigmoid" and "noisy-OR" varieties of stochastic feedforward network. These networks are in the same class as the "belief networks" used in expert systems. They represent a probability distribution over a set...	[ 954 ]	Validation
970	1	Title: Generality versus Size in Genetic Programming Abstract: Genetic Programming (GP) uses variable size representations as programs. Size becomes an important and interesting emergent property of the structures evolved by GP. The size of programs can be both a controlling and a controlled factor in GP search. Size...	[ 567, 974, 1378, 1396, 1533 ]	Train
971	3	Title: Decision-Theoretic Foundations for Causal Reasoning Abstract: We present a definition of cause and effect in terms of decision-theoretic primitives and thereby provide a principled foundation for causal reasoning. Our definition departs from the traditional view of causation in that causal assertions may vary ...	[ 1326, 1527, 1602, 2166 ]	Train
972	1	Title: On Genetic Programming of Fuzzy Rule-Based Systems for Intelligent Control Abstract: Fuzzy logic and evolutionary computation have proven to be convenient tools for handling real-world uncertainty and designing control systems, respectively. An approach is presented that combines attributes of these paradigms ...	[ 953 ]	Test
973	3	Title: Interval Censored Survival Data: A Review of Recent Progress Abstract: We review estimation in interval censoring models, including nonparametric estimation of a distribution function and estimation of regression models. In the non-parametric setting, we describe computational procedures and asymptotic propert...	[ 802, 993 ]	Test
974	1	Title: Balancing Accuracy and Parsimony in Genetic Programming 1 Abstract: Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of fixed length. The flexible representation scheme is very important because it allows the...	[ 844, 970 ]	Train
975	2	Title: State Reconstruction for Determining Predictability in Driven Nonlinear Acoustical Systems Abstract: Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of fixed length. The flexible representation scheme is ver...	[ 74, 76, 608, 668, 1079 ]	Test
976	3	Title: Space-efficient inference in dynamic probabilistic networks Abstract: Dynamic probabilistic networks (DPNs) are a useful tool for modeling complex stochastic processes. The simplest inference task in DPNs is monitoring \| that is, computing a posterior distribution for the state variables at each time step give...	[ 905, 1268, 1287, 1393 ]	Test
977	6	Title: Pessimistic and Optimistic Induction Abstract: Learning methods vary in the optimism or pessimism with which they regard the informativeness of learned knowledge. Pessimism is implicit in hypothesis testing, where we wish to draw cautious conclusions from experimental evidence. However, this paper demonstrates...	[ 1234 ]	Train
978	2	Title: Hierarchical Recurrent Neural Networks for Long-Term Dependencies Abstract: We have already shown that extracting long-term dependencies from sequential data is difficult, both for deterministic dynamical systems such as recurrent networks, and probabilistic models such as hidden Markov models (HMMs) or input/...	[ 664, 1593, 1825, 1954 ]	Train
979	2	Title: FLAT MINIMA Neural Computation 9(1):1-42 (1997) Abstract: We present a new algorithm for finding low complexity neural networks with high generalization capability. The algorithm searches for a "flat" minimum of the error function. A flat minimum is a large connected region in weight-space where the error rema...	[ 68, 157, 766, 879, 1825, 1845 ]	Train
980	2	Title: Some Topics in Neural Networks and Control Abstract: We present a new algorithm for finding low complexity neural networks with high generalization capability. The algorithm searches for a "flat" minimum of the error function. A flat minimum is a large connected region in weight-space where the error remains a...	[ 1488 ]	Train
981	0	Title: AN ENHANCER FOR REACTIVE PLANS Abstract: This paper describes our method for improving the comprehensibility, accuracy, and generality of reactive plans. A reactive plan is a set of reactive rules. Our method involves two phases: (1) formulate explanations of execution traces, and then (2) generate new reactiv...	[ 902, 910, 965, 1140 ]	Train
982	1	Title: Evolutionary Neural Networks for Value Ordering in Constraint Satisfaction Problems Abstract: Technical Report AI94-218 May 1994 Abstract A new method for developing good value-ordering strategies in constraint satisfaction search is presented. Using an evolutionary technique called SANE, in which individual n...	[ 163, 247, 910 ]	Train
983	0	Title: Refining Conversational Case Libraries Abstract: Conversational case-based reasoning (CBR) shells (e.g., Inference's CBR Express) are commercially successful tools for supporting the development of help desk and related applications. In contrast to rule-based expert systems, they capture knowledge as cases rat...	[ 256, 887, 928, 1002, 1302, 1531, 1636, 1735 ]	Train
984	0	Title: A Computational Account of Movement Learning and its Impact on the Speed-Accuracy Tradeoff Abstract: We present a computational model of movement skill learning. The types of skills addressed are a class of trajectory following movements involving multiple accelerations, decelerations and changes in direction ...	[ 1048 ]	Train
985	3	Title: Combining Symbolic and Connectionist Learning Methods to Refine Certainty-Factor Rule-Bases Abstract: We present a computational model of movement skill learning. The types of skills addressed are a class of trajectory following movements involving multiple accelerations, decelerations and changes in direction...	[ 159, 1102, 2038, 2066 ]	Train
986	0	Title: Improving accuracy by combining rule-based and case-based reasoning Abstract: An architecture is presented for combining rule-based and case-based reasoning. The architecture is intended for domains that are understood reasonably well, but still imperfectly. It uses a set of rules, which are taken to be only a...	[ 136, 862, 1642, 1644, 2484, 2605, 2614, 2616 ]	Train
987	3	Title: Stacked Density Estimation Abstract: Technical Report No. 97-36, Information and Computer Science Department, University of California, Irvine	[ 74, 1240, 1608 ]	Train
988	4	Title: A COMPARISON OF Q-LEARNING AND CLASSIFIER SYSTEMS Abstract: Reinforcement Learning is a class of problems in which an autonomous agent acting in a given environment improves its behavior by progressively maximizing a function calculated just on the basis of a succession of scalar responses received from the en...	[ 1515 ]	Train
989	2	Title: Finding Compact and Sparse Distributed Representations of Visual Images Abstract: Some recent work has investigated the dichotomy between compact coding using dimensionality reduction and sparse distributed coding in the context of understanding biological information processing. We introduce an artificial neu...	[ 1068, 1418 ]	Train
990	2	Title: Shattering all sets of k points in "general position" requires (k 1)=2 parameters Abstract: For classes of concepts defined by certain classes of analytic functions depending on n parameters, there are nonempty open sets of samples of length 2n + 2 which cannot be shattered. A slighly weaker result is also pro...	[ 58, 805, 1774 ]	Test
991	0	Title: Systematic Evaluation of Design Decisions in CBR Systems Abstract: Two important goals in the evaluation of an AI theory or model are to assess the merit of the design decisions in the performance of an implemented computer system and to analyze the impact in the performance when the system faces problem domai...	[ 318, 858, 1084, 1368 ]	Train
992	0	Title: Adapting Abstract Knowledge Abstract: For a case-based reasoner to use its knowledge flexibly, it must be equipped with a powerful case adapter. A case-based reasoner can only cope with variation in the form of the problems it is given to the extent that its cases in memory can be efficiently adapted to fit a ...	[ 1354 ]	Train
993	3	Title: Efficient Estimation for the Cox Model with Interval Censoring Abstract: The maximum likelihood estimator (MLE) for the proportional hazards model with current status data is studied. It is shown that the MLE for the regression parameter is asymptotically normal with p n-convergence rate and achieves the infor...	[ 973 ]	Validation

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