node_id int64 0 76.9k | label int64 0 39 | text stringlengths 13 124k | neighbors listlengths 0 3.32k | mask stringclasses 4
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|---|---|---|---|---|
1,494 | 2 | Title: Avoiding Saturation By Trajectory Reparameterization
Abstract: The problem of trajectory tracking in the presence of input constraints is considered. The desired trajectory is reparameterized on a slower time scale in order to avoid input saturation. Necessary conditions that the reparameterizing function must... | [
1282
] | Validation |
1,495 | 1 | Title: Clique Detection via Genetic Programming Topics in Combinatorial Optimization
Abstract: Genetic Programming is utilized as a technique for detecting cliques in a network. Candidate cliques are represented in lists, and the lists are manipulated such that larger cliques are formed from the candidates. The cliq... | [
995,
1231
] | Validation |
1,496 | 0 | Title: Adaptive Similarity Assessment for Case-Based Explanation.*
Abstract: Genetic Programming is utilized as a technique for detecting cliques in a network. Candidate cliques are represented in lists, and the lists are manipulated such that larger cliques are formed from the candidates. The clique detection proble... | [
1125
] | Train |
1,497 | 0 | Title: Combining Rules and Cases to Learn Case Adaptation
Abstract: Computer models of case-based reasoning (CBR) generally guide case adaptation using a fixed set of adaptation rules. A difficult practical problem is how to identify the knowledge required to guide adaptation for particular tasks. Likewise, an open i... | [
580,
901,
1126,
1163,
1212
] | Test |
1,498 | 0 | Title: INFERENTIAL THEORY OF LEARNING: Developing Foundations for Multistrategy Learning
Abstract: The development of multistrategy learning systems should be based on a clear understanding of the roles and the applicability conditions of different learning strategies. To this end, this chapter introduces the Inferen... | [
163,
289,
582,
1049,
1071,
1163,
1351,
1483,
1534,
2158,
2398,
2450
] | Validation |
1,499 | 2 | Title: Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers
Abstract: The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as s... | [
611,
1050,
1306,
1310
] | Train |
1,500 | 6 | Title: On the Induction of Intelligible Ensembles
Abstract: Ensembles of classifiers, e.g. decision trees, often exhibit greater predictive accuracy than single classifiers alone. Bagging and boosting are two standard ways of generating and combining multiple classifiers. Unfortunately, the increase in predictive per... | [
1238,
1267,
1484,
2180
] | Train |
1,501 | 2 | Title: A Characterization of Integral Input to State Stability
Abstract: Just as input to state stability (iss) generalizes the idea of finite gains with respect to supremum norms, the new notion of integral input to state stability (iiss) generalizes the concept of finite gain when using an integral norm on inputs. ... | [
447,
693,
1471
] | Validation |
1,502 | 3 | Title: Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View
Abstract: The use of graphs to represent independence structure in multivariate probability models has been pursued in a relatively independent fashion across a wide variety of research disciplines since the beginning of this cent... | [
577,
772,
1288,
1393
] | Validation |
1,503 | 3 | Title: Belief Revision in Probability Theory
Abstract: In a probability-based reasoning system, Bayes' theorem and its variations are often used to revise the system's beliefs. However, if the explicit conditions and the implicit conditions of probability assignments are properly distinguished, it follows that Bayes'... | [
1108,
1276,
1308,
1504,
1507,
1525
] | Train |
1,504 | 3 | Title: From Inheritance Relation to Non-Axiomatic Logic
Abstract: At the beginning of the paper, three binary term logics are defined. The first is based only on an inheritance relation. The second and the third suggest a novel way to process extension and intension, and they also have interesting relations with Aris... | [
1108,
1276,
1308,
1415,
1503,
1506,
1507,
1525
] | Train |
1,505 | 6 | Title: Probably Approximately Optimal Derivation Strategies
Abstract: An inference graph can have many "derivation strategies", each a particular ordering of the steps involved in reducing a given query to a sequence of database retrievals. An "optimal strategy" for a given distribution of queries is a complete strat... | [
251,
865,
932,
2560
] | Train |
1,506 | 3 | Title: Non-Axiomatic Reasoning System (Version 2.2) used to show how the system works. The limitations of
Abstract: NARS uses a new form of term logic, or an extended syllogism, in which several types of uncertainties can be represented and processed, and in which deduction, induction, abduction, and revision are carri... | [
1108,
1276,
1308,
1415,
1504,
1507,
1525
] | Train |
1,507 | 3 | Title: A Unified Treatment of Uncertainties
Abstract: Uncertainty in artificial intelligence" is an active research field, where several approaches have been suggested and studied for dealing with various types of uncertainty. However, it's hard to rank the approaches in general, because each of them is usually aimed... | [
1108,
1308,
1503,
1504,
1506
] | Train |
1,508 | 2 | Title: Segmenting Time Series using Gated Experts with Simulated Annealing
Abstract: Many real-world time series are multi-stationary, where the underlying data generating process (DGP) switches between different stationary subprocesses, or modes of operation. An important problem in modeling such systems is to disco... | [
668,
1724
] | Validation |
1,509 | 2 | Title: Synthetic Aperture Radar Processing by a Multiple Scale Neural System for Boundary and Surface Representation
Abstract: Many real-world time series are multi-stationary, where the underlying data generating process (DGP) switches between different stationary subprocesses, or modes of operation. An important pr... | [
589,
592,
1144
] | Train |
1,510 | 6 | Title: The Problem with Noise and Small Disjuncts
Abstract: Systems that learn from examples often create a disjunctive concept definition. The disjuncts in the concept definition which cover only a few training examples are referred to as small disjuncts. The problem with small disjuncts is that they are more error ... | [
790,
1234,
2057
] | Validation |
1,511 | 2 | Title: In Stable Dynamic Parameter Adaptation
Abstract: | [
1357
] | Train |
1,512 | 3 | Title: Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule
Abstract: A training set of data has been used to construct a rule for predicting future responses. What is the error rate of this rule? The traditional answer to this question is given by cross-validation. The cross-validation ... | [
949,
999,
1087,
1112,
1235,
1267,
1335,
1463,
1608,
1671
] | Train |
1,513 | 0 | Title: Fast NP Chunking Using Memory-Based Learning Techniques
Abstract: In this paper we discuss the application of Memory-Based Learning (MBL) to fast NP chunking. We first discuss the application of a fast decision tree variant of MBL (IGTree) on the dataset described in (Ramshaw and Marcus, 1995), which consists ... | [
634,
785,
862,
1328,
1812
] | Train |
1,514 | 6 | Title: Is Consistency Harmful?
Abstract: We examine the issue of consistency from a new perspective. To avoid overfitting the training data, a considerable number of current systems have sacrificed the goal of learning hypotheses that are perfectly consistent with the training instances by setting a new goal of hypot... | [
429,
1333
] | Test |
1,515 | 4 | Title: Evolving Optimal Populations with XCS Classifier Systems
Abstract: We examine the issue of consistency from a new perspective. To avoid overfitting the training data, a considerable number of current systems have sacrificed the goal of learning hypotheses that are perfectly consistent with the training instanc... | [
163,
936,
961,
988,
1447,
1581,
1711
] | Train |
1,516 | 1 | Title: Solving 3-SAT by GAs Adapting Constraint Weights
Abstract: Handling NP complete problems with GAs is a great challenge. In particular the presence of constraints makes finding solutions hard for a GA. In this paper we present a problem independent constraint handling mechanism, Stepwise Adaptation of Weights (... | [
833,
1136,
1218
] | Train |
1,517 | 2 | Title: Robust Interpretation of Neural-Network Models
Abstract: Artificial Neural Network seem very promising for regression and classification, especially for large covariate spaces. These methods represent a non-linear function as a composition of low dimensional ridge functions and therefore appear to be less sens... | [
1612
] | Validation |
1,518 | 1 | Title: Feature selection through Functional Links with Evolutionary Computation for Neural Networks
Abstract: In this paper we describe different ways to select and transform features using evolutionary computation. The features are intended to serve as inputs to a feedforward network. The first way is the selection ... | [
1536
] | Train |
1,519 | 5 | Title: What online Machine Learning can do for Knowledge Acquisition A Case Study
Abstract: This paper reports on the development of a realistic knowledge-based application using the MOBAL system. Some problems and requirements resulting from industrial-caliber tasks are formulated. A step-by-step account of the cons... | [
963,
1177
] | Train |
1,520 | 2 | Title: Equivariant adaptive source separation
Abstract: Source separation consists in recovering a set of independent signals when only mixtures with unknown coefficients are observed. This paper introduces a class of adaptive algorithms for source separation which implements an adaptive version of equivariant estima... | [
59,
354,
570,
834,
839,
872,
873,
874,
920,
1072,
1200,
1211,
1245,
1246,
1258,
1524,
1526,
1709
] | Train |
1,521 | 6 | Title: Improving Bagging Performance by Increasing Decision Tree Diversity
Abstract: Ensembles of decision trees often exhibit greater predictive accuracy than single trees alone. Bagging and boosting are two standard ways of generating and combining multiple trees. Boosting has been empirically determined to be the ... | [
70,
1185,
1237,
1484
] | Train |
1,522 | 6 | Title: Improving Bagging Performance by Increasing Decision Tree Diversity
Abstract: ARCING THE EDGE Leo Breiman Technical Report 486 , Statistics Department University of California, Berkeley CA. 94720 Abstract Recent work has shown that adaptively reweighting the training set, growing a classifier using the new wei... | [
569,
1484
] | Validation |
1,523 | 1 | Title: A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms
Abstract: In order to sequence the tasks of a job shop problem (JSP) on a number of machines related to the technological machine order of jobs, a new representation technique mathematically known as "permutation with repetition"... | [
343,
813,
815,
880,
1060,
1136
] | Train |
1,524 | 2 | Title: BLIND SEPARATION OF DELAYED SOURCES BASED ON INFORMATION MAXIMIZATION
Abstract: Recently, Bell and Sejnowski have presented an approach to blind source separation based on the information maximization principle. We extend this approach into more general cases where the sources may have been delayed with respec... | [
570,
576,
1243,
1245,
1381,
1520,
1526
] | Validation |
1,525 | 3 | Title: Reference Classes and Multiple Inheritances
Abstract: The reference class problem in probability theory and the multiple inheritances (extensions) problem in non-monotonic logics can be referred to as special cases of conflicting beliefs. The current solution accepted in the two domains is the specificity prio... | [
1276,
1415,
1503,
1504,
1506
] | Train |
1,526 | 2 | Title: Working Paper IS-97-22 (Information Systems) A First Application of Independent Component Analysis to Extracting Structure
Abstract: This paper discusses the application of a modern signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time... | [
576,
839,
1520,
1524
] | Train |
1,527 | 3 | Title: A THEORY OF INFERRED CAUSATION perceive causal relationships in uncon trolled observations. 2. the task
Abstract: This paper concerns the empirical basis of causation, and addresses the following issues: We propose a minimal-model semantics of causation, and show that, contrary to common folklore, genuine causal... | [
211,
260,
419,
827,
909,
971,
1086,
1240,
1543,
1747,
1894,
2076,
2088,
2166,
2221,
2420,
2524,
2525,
2561
] | Validation |
1,528 | 5 | Title: Using Qualitative Models to Guide Inductive Learning
Abstract: This paper presents a method for using qualitative models to guide inductive learning. Our objectives are to induce rules which are not only accurate but also explainable with respect to the qualitative model, and to reduce learning time by exploit... | [
151,
426,
1487
] | Train |
1,529 | 4 | Title: Explanation Based Learning: A Comparison of Symbolic and Neural Network Approaches
Abstract: Explanation based learning has typically been considered a symbolic learning method. An explanation based learning method that utilizes purely neural network representations (called EBNN) has recently been developed, a... | [
565,
882,
1314
] | Validation |
1,530 | 1 | Title: Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems
Abstract: The multi-parent scanning crossover, generalizing the traditional uniform crossover, and diagonal crossover, generalizing 1-point (n-point) crossovers, were introduced in [5]. In subsequent publications, see [... | [
145,
163,
1218,
1424,
2089
] | Train |
1,531 | 0 | Title: NACODAE: Navy Conversational Decision Aids Environment
Abstract: This report documents NACODAE, the Navy Conversational Decision Aids Environment being developed at the Navy Center for Applied Research in Artificial Intelligence (NCARAI), which is a branch of the Naval Research Laboratory. NA-CODAE is a softwa... | [
66,
887,
983,
1154
] | Test |
1,532 | 3 | Title: Automated Decomposition of Model-based Learning Problems
Abstract: A new generation of sensor rich, massively distributed autonomous systems is being developed that has the potential for unprecedented performance, such as smart buildings, reconfigurable factories, adaptive traffic systems and remote earth ecos... | [
327,
558,
577,
782,
925,
1480,
1487
] | Test |
1,533 | 1 | Title: Evolving Visual Routines
Abstract: Traditional machine vision assumes that the vision system recovers a a complete, labeled description of the world [ Marr, 1982 ] . Recently, several researchers have criticized this model and proposed an alternative model which considers perception as a distributed collection... | [
781,
846,
900,
970,
1277,
1730
] | Train |
1,534 | 0 | Title: The Use of Explicit Goals for Knowledge to Guide Inference and Learning
Abstract: Combinatorial explosion of inferences has always been a central problem in artificial intelligence. Although the inferences that can be drawn from a reasoner's knowledge and from available inputs is very large (potentially infini... | [
289,
1122,
1148,
1163,
1278,
1498,
1556,
1597
] | Validation |
1,535 | 0 | Title: Decision Models: A Theory of Volitional Explanation
Abstract: This paper presents a theory of motivational analysis, the construction of volitional explanations to describe the planning behavior of agents. We discuss both the content of such explanations, as well as the process by which an understander builds ... | [
289,
629,
1348,
1537
] | Train |
1,536 | 1 | Title: Representation and Evolution of Neural Networks
Abstract: An evolutionary approach for developing improved neural network architectures is presented. It is shown that it is possible to use genetic algorithms for the construction of backpropagation networks for real world tasks. Therefore a network representati... | [
163,
207,
1518,
1728,
2504
] | Test |
1,537 | 0 | Title: Incremental Learning of Explanation Patterns and their Indices
Abstract: This paper describes how a reasoner can improve its understanding of an incompletely understood domain through the application of what it already knows to novel problems in that domain. Recent work in AI has dealt with the issue of using ... | [
289,
629,
1348,
1535,
1556
] | Train |
1,538 | 2 | Title: Analysis of Drifting Dynamics with Neural Network Hidden Markov Models
Abstract: We present a method for the analysis of nonstationary time series with multiple operating modes. In particular, it is possible to detect and to model both a switching of the dynamics and a less abrupt, time consuming drift from on... | [
1724
] | Train |
1,539 | 5 | Title: Finding new rules for incomplete theories: Explicit biases for induction with contextual information. In Proceedings
Abstract: addressed in KBANN (which translates a theory into a neural-net, refines it using backpropagation, and then retranslates the result back into rules) by adding extra hidden units and conn... | [
136,
378,
449,
638,
924,
2091
] | Train |
1,540 | 4 | Title: MultiPlayer Residual Advantage Learning With General Function Approximation
Abstract: A new algorithm, advantage learning, is presented that improves on advantage updating by requiring that a single function be learned rather than two. Furthermore, advantage learning requires only a single type of update, the ... | [
565,
842,
1045,
1118,
1378,
1443,
1459
] | Train |
1,541 | 1 | Title: Unsupervised Learning with the Soft-Means Algorithm
Abstract: This note describes a useful adaptation of the `peak seeking' regime used in unsupervised learning processes such as competitive learning and `k-means'. The adaptation enables the learning to capture low-order probability effects and thus to more fu... | [
1395
] | Test |
1,542 | 0 | Title: Protein Sequencing Experiment Planning Using Analogy protein sequencing experiments. Planning is interleaved with experiment execution,
Abstract: Experiment design and execution is a central activity in the natural sciences. The SeqER system provides a general architecture for the integration of automated planni... | [
801
] | Test |
1,543 | 3 | Title: Belief Networks Revisited
Abstract: Experiment design and execution is a central activity in the natural sciences. The SeqER system provides a general architecture for the integration of automated planning techniques with a variety of domain knowledge in order to plan scientific experiments. These planning tec... | [
1324,
1527
] | Train |
1,544 | 1 | Title: Monitoring in Embedded Agents
Abstract: Finding good monitoring strategies is an important process in the design of any embedded agent. We describe the nature of the monitoring problem, point out what makes it difficult, and show that while periodic monitoring strategies are often the easiest to derive, they a... | [
163,
566,
1206
] | Train |
1,545 | 3 | Title: Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management
Abstract: This paper discusses issues related to Bayesian network model learning for unbalanced binary classification tasks. In general, the primary focus of current research on Bayesian network learning systems (e.g., K2 and its v... | [
1086,
1582,
1909
] | Validation |
1,546 | 4 | Title: Analytical Mean Squared Error Curves in Temporal Difference Learning
Abstract: We have calculated analytical expressions for how the bias and variance of the estimators provided by various temporal difference value estimation algorithms change with o*ine updates over trials in absorbing Markov chains using loo... | [
63,
565,
1376
] | Validation |
1,547 | 2 | Title: Misclassification Minimization
Abstract: The problem of minimizing the number of misclassified points by a plane, attempting to separate two point sets with intersecting convex hulls in n-dimensional real space, is formulated as a linear program with equilibrium constraints (LPEC). This general LPEC can be con... | [
142,
227,
427,
1283
] | Train |
1,548 | 3 | Title: Free energy coding In
Abstract: In this paper, we introduce a new approach to the problem of optimal compression when a source code produces multiple codewords for a given symbol. It may seem that the most sensible codeword to use in this case is the shortest one. However, in the proposed free energy approach... | [
76,
1291,
1374
] | Train |
1,549 | 3 | Title: Explaining Predictions in Bayesian Networks and Influence Diagrams
Abstract: As Bayesian Networks and Influence Diagrams are being used more and more widely, the importance of an efficient explanation mechanism becomes more apparent. We focus on predictive explanations, the ones designed to explain predictions... | [
339,
1602
] | Train |
1,550 | 6 | Title: MDL and MML Similarities and Differences (Introduction to Minimum Encoding Inference Part III)
Abstract: Tech Report 207 Department of Computer Science, Monash University, Clayton, Vic. 3168, Australia Abstract: This paper continues the introduction to minimum encoding inductive inference given by Oliver and H... | [
84,
157,
684,
1158,
1199,
1238,
1419,
1425,
1427,
1555,
1624
] | Train |
1,551 | 2 | Title: A Performance Analysis of the CNS-1 on Large, Dense Backpropagation Networks Connectionist Network Supercomputer
Abstract: We determine in this study the sustained performance of the CNS-1 during training and evaluation of large multilayered feedforward neural networks. Using a sophisticated coding, the 128-no... | [
272,
914
] | Test |
1,552 | 0 | Title: on Case-Based Reasoning Integrations Case-Based Seeding for an Interactive Crisis Response Assistant
Abstract: In this paper, we present an interactive, case-based approach to crisis response that provides users with the ability to rapidly develop good responses while allowing them to retain ultimate control o... | [
1212,
1553,
1554
] | Test |
1,553 | 0 | Title: Learning to Predict User Operations for Adaptive Scheduling
Abstract: Mixed-initiative systems present the challenge of finding an effective level of interaction between humans and computers. Machine learning presents a promising approach to this problem in the form of systems that automatically adapt their be... | [
82,
901,
1552,
1554
] | Train |
1,554 | 0 | Title: CABINS A Framework of Knowledge Acquisition and Iterative Revision for Schedule Improvement and Reactive Repair
Abstract: Mixed-initiative systems present the challenge of finding an effective level of interaction between humans and computers. Machine learning presents a promising approach to this problem in t... | [
901,
951,
1401,
1552,
1553
] | Train |
1,555 | 3 | Title: Bayesian and Information-Theoretic Priors for Bayesian Network Parameters
Abstract: We consider Bayesian and information-theoretic approaches for determining non-informative prior distributions in a parametric model family. The information-theoretic approaches are based on the recently modified definition of s... | [
558,
1158,
1550
] | Train |
1,556 | 0 | Title: A Goal-Based Approach to Intelligent Information Retrieval
Abstract: Intelligent information retrieval (IIR) requires inference. The number of inferences that can be drawn by even a simple reasoner is very large, and the inferential resources available to any practical computer system are limited. This problem... | [
1534,
1537
] | Test |
1,557 | 4 | Title: Using Communication to Reduce Locality in Distributed Multi-Agent Learning
Abstract: This paper attempts to bridge the fields of machine learning, robotics, and distributed AI. It discusses the use of communication in reducing the undesirable effects of locality in fully distributed multi-agent systems with mu... | [
650,
691,
1649,
1687
] | Validation |
1,558 | 1 | Title: How good are genetic algorithms at finding large cliques: an experimental study
Abstract: This paper investigates the power of genetic algorithms at solving the MAX-CLIQUE problem. We measure the performance of a standard genetic algorithm on an elementary set of problem instances consisting of embedded clique... | [
163,
1136,
2564
] | Test |
1,559 | 2 | Title: In Active Learning with Statistical Models
Abstract: For many types of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992; Cohn, 1994]. We then show how the same principles may be used to select ... | [
71,
740,
929,
1664,
1683,
1697,
1703,
1791,
2658
] | Train |
1,560 | 4 | Title: DESIGN AND ANALYSIS OF EFFICIENT REINFORCEMENT LEARNING ALGORITHMS
Abstract: For many types of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992; Cohn, 1994]. We then show how the same principles... | [
456,
535,
791,
1161,
2198
] | Train |
1,561 | 6 | Title: Characterizing Rational versus Exponential Learning Curves
Abstract: We consider the standard problem of learning a concept from random examples. Here a learning curve can be defined to be the expected error of a learner's hypotheses as a function of training sample size. Haussler, Littlestone and Warmuth have... | [
967
] | Test |
1,562 | 2 | Title: Using Sampling and Queries to Extract Rules from Trained Neural Networks
Abstract: Concepts learned by neural networks are difficult to understand because they are represented using large assemblages of real-valued parameters. One approach to understanding trained neural networks is to extract symbolic rules t... | [
355,
627,
631,
1637
] | Test |
1,563 | 1 | Title: Fast EquiPartitioning of Rectangular Domains using Stripe Decomposition
Abstract: This paper presents a fast algorithm that provides optimal or near optimal solutions to the minimum perimeter problem on a rectangular grid. The minimum perimeter problem is to partition a grid of size M N into P equal area regio... | [
53,
357,
803
] | Test |
1,564 | 2 | Title: GROWING RADIAL BASIS FUNCTION NETWORKS
Abstract: This paper presents and evaluates two algorithms for incrementally constructing Radial Basis Function Networks, a class of neural networks which looks more suitable for adtaptive control applications than the more popular backpropagation networks. The first algo... | [
611,
687,
745,
899,
1672
] | Validation |
1,565 | 1 | Title: Evolving Fuzzy Prototypes for Efficient Data Clustering
Abstract: number of prototypes used to represent each class, the position of each prototype within its class and the membership function associated with each prototype. This paper proposes a novel, evolutionary approach to data clustering and classificati... | [
899,
1088
] | Test |
1,566 | 6 | Title: Worst-case Quadratic Loss Bounds for Prediction Using Linear Functions and Gradient Descent
Abstract: In this paper we study the performance of gradient descent when applied to the problem of on-line linear prediction in arbitrary inner product spaces. We prove worst-case bounds on the sum of the squared predi... | [
453,
1124,
1567
] | Test |
1,567 | 6 | Title: Improved Bounds about On-line Learning of Smooth Functions of a Single Variable
Abstract: We consider the complexity of learning classes of smooth functions formed by bounding different norms of a function's derivative. The learning model is the generalization of the mistake-bound model to continuous-valued fu... | [
1124,
1358,
1566
] | Train |
1,568 | 0 | Title: The Utility of Feature Weighting in Nearest-Neighbor Algorithms
Abstract: Nearest-neighbor algorithms are known to depend heavily on their distance metric. In this paper, we investigate the use of a weighted Euclidean metric in which the weight for each feature comes from a small set of options. We describe Di... | [
430,
1053,
1328
] | Train |
1,569 | 5 | Title: Estimating Attributes: Analysis and Extensions of RELIEF
Abstract: In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them. Kira and Rendell (1992a,b) developed an algorithm called RELIEF, which was show... | [
208,
430,
877,
1008,
1010,
1011,
1073,
1182,
1327,
1486,
1587,
1609,
1679,
1684,
1721,
1726
] | Train |
1,570 | 0 | Title: Average-Case Analysis of a Nearest Neighbor Algorithm
Abstract: In this paper we present an average-case analysis of the nearest neighbor algorithm, a simple induction method that has been studied by many researchers. Our analysis assumes a conjunctive target concept, noise-free Boolean attributes, and a unifo... | [
634,
1109,
1111,
1164,
1339,
1678
] | Train |
1,571 | 1 | Title: Average-Case Analysis of a Nearest Neighbor Algorithm
Abstract: Eugenic Evolution for Combinatorial Optimization John William Prior Report AI98-268 May 1998 | [
163,
343,
1216,
1218,
2202
] | Train |
1,572 | 1 | Title: The Coevolution of Mutation Rates
Abstract: In order to better understand life, it is helpful to look beyond the envelop of life as we know it. A simple model of coevolution was implemented with the addition of genes for longevity and mutation rate in the individuals. This made it possible for a lineage to evo... | [
163,
1139,
1598
] | Train |
1,573 | 4 | Title: Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task
Abstract: difficult. We face this problem using an architecture based on learning classifier systems and on the description of the learning technique used and of the organizational structure proposed... | [
163,
636,
764,
910,
1432,
1481,
1673,
2174
] | Train |
1,574 | 0 | Title: Probabilistic Instance-Based Learning
Abstract: Traditional instance-based learning methods base their predictions directly on (training) data that has been stored in the memory. The predictions are based on weighting the contributions of the individual stored instances by a distance function implementing a do... | [
484,
1017
] | Test |
1,575 | 1 | Title: A Comparative Study of Genetic Search
Abstract: We present a comparative study of genetic algorithms and their search properties when treated as a combinatorial optimization technique. This is done in the context of the NP-hard problem MAX-SAT, the comparison being relative to the Metropolis process, and by ex... | [
163,
1136,
1305
] | Train |
1,576 | 6 | Title: What do Constructive Learners Really Learn?
Abstract: In constructive induction (CI), the learner's problem representation is modified as a normal part of the learning process. This may be necessary if the initial representation is inadequate or inappropriate. However, the distinction between constructive and ... | [
375,
426,
1266,
1595
] | Validation |
1,577 | 1 | Title: Fast Probabilistic Modeling for Combinatorial Optimization
Abstract: Probabilistic models have recently been utilized for the optimization of large combinatorial search problems. However, complex probabilistic models that attempt to capture inter-parameter dependencies can have prohibitive computational costs.... | [
343,
427,
658,
1580
] | Train |
1,578 | 5 | Title: SFOIL: Stochastic Approach to Inductive Logic Programming
Abstract: Current systems in the field of Inductive Logic Programming (ILP) use, primarily for the sake of efficiency, heuristically guided search techniques. Such greedy algorithms suffer from local optimization problem. Present paper describes a syste... | [
877,
1010,
1061,
1182,
1622,
1651
] | Test |
1,579 | 2 | Title: A Radial Basis Function Approach to Financial Time Series Analysis
Abstract: Current systems in the field of Inductive Logic Programming (ILP) use, primarily for the sake of efficiency, heuristically guided search techniques. Such greedy algorithms suffer from local optimization problem. Present paper describe... | [
1103
] | Test |
1,580 | 1 | Title: MIMIC: Finding Optima by Estimating Probability Densities
Abstract: In many optimization problems, the structure of solutions reflects complex relationships between the different input parameters. For example, experience may tell us that certain parameters are closely related and should not be explored indepen... | [
689,
1577,
1625
] | Train |
1,581 | 4 | Title: A Study of the Generalization Capabilities of XCS
Abstract: We analyze the generalization behavior of the XCS classifier system in environments in which only a few generalizations can be done. Experimental results presented in the paper evidence that the generalization mechanism of XCS can prevent it from lear... | [
657,
764,
1447,
1515,
1711
] | Train |
1,582 | 3 | Title: Efficient Learning of Selective Bayesian Network Classifiers
Abstract: In this paper, we present a computation-ally efficient method for inducing selective Bayesian network classifiers. Our approach is to use information-theoretic metrics to efficiently select a subset of attributes from which to learn the cla... | [
632,
1086,
1545,
1908,
1909,
2017,
2677
] | Test |
1,583 | 2 | Title: Evolutionary Design of Neural Architectures A Preliminary Taxonomy and Guide to Literature
Abstract: In this paper, we present a computation-ally efficient method for inducing selective Bayesian network classifiers. Our approach is to use information-theoretic metrics to efficiently select a subset of attribut... | [
900,
2396,
2563
] | Validation |
1,584 | 0 | Title: Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the
Abstract: The effectiveness of a case-based reasoning system is known to depend critically on its similarity measure. However, it is not clear whether there are elusive and esoteric similarity measures which might impro... | [
1164,
1328,
1626,
2037,
2151
] | Validation |
1,585 | 4 | Title: Q-Learning for Bandit Problems
Abstract: Multi-armed bandits may be viewed as decompositionally-structured Markov decision processes (MDP's) with potentially very-large state sets. A particularly elegant methodology for computing optimal policies was developed over twenty ago by Gittins [Gittins & Jones, 1974]... | [
565,
738,
804
] | Train |
1,586 | 6 | Title: On the Boosting Ability of Top-Down Decision Tree Learning Algorithms provably optimal for decision tree
Abstract: We analyze the performance of top-down algorithms for decision tree learning, such as those employed by the widely used C4.5 and CART software packages. Our main result is a proof that such algorith... | [
1388
] | Train |
1,587 | 5 | Title: A counter example to the stronger version of the binary tree hypothesis
Abstract: The paper describes a counter example to the hypothesis which states that a greedy decision tree generation algorithm that constructs binary decision trees and branches on a single attribute-value pair rather than on all values o... | [
1569
] | Train |
1,588 | 1 | Title: Automatic Modularization by Speciation
Abstract: Real-world problems are often too difficult to be solved by a single monolithic system. There are many examples of natural and artificial systems which show that a modular approach can reduce the total complexity of the system while solving a difficult problem s... | [
1114,
1117,
2334
] | Train |
1,589 | 4 | Title: Learning to Sense Selectively in Physical Domains
Abstract: In this paper we describe an approach to representing, using, and improving sensory skills for physical domains. We present Icarus, an architecture that represents control knowledge in terms of durative states and sequences of such states. The system ... | [
910
] | Test |
1,590 | 1 | Title: The Exploitation of Cooperation in Iterated Prisoner's Dilemma
Abstract: We follow Axelrod [2] in using the genetic algorithm to play Iterated Prisoner's Dilemma. Each member of the population (i.e., each strategy) is evaluated by how it performs against the other members of the current population. This create... | [
163,
910,
965
] | Train |
1,591 | 2 | Title: Unsupervised Learning by Convex and Conic Coding
Abstract: Unsupervised learning algorithms based on convex and conic encoders are proposed. The encoders find the closest convex or conic combination of basis vectors to the input. The learning algorithms produce basis vectors that minimize the reconstruction er... | [
33,
36,
871,
954,
1050,
1701
] | Train |
1,592 | 2 | Title: A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction
Abstract: Although recurrent neural nets have been moderately successful in learning to emulate finite-state machines (FSMs), the continuous internal state dynamics of a neural net are not well matched to the discrete behavio... | [
405,
1161,
1176,
1293,
1298,
1734
] | Train |
1,593 | 3 | Title: Boltzmann Chains and Hidden Markov Models
Abstract: We propose a statistical mechanical framework for the modeling of discrete time series. Maximum likelihood estimation is done via Boltzmann learning in one-dimensional networks with tied weights. We call these networks Boltzmann chains and show that they cont... | [
978,
1116,
1288,
1437,
1461
] | Validation |
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