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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|---|---|---|---|---|
1,194 | 0 | Title: An Explanation-Based Approach to Improve Retrieval in Case-Based Planning
Abstract: When a case-based planner is retrieving a previous case in preparation for solving a new similar problem, it is often not aware of the implicit features of the new problem situation which determine if a particular case may be s... | [
594,
1122,
1621
] | Test |
1,195 | 2 | Title: Statistical Evaluation of Neural Network Experiments: Minimum Requirements and Current Practice
Abstract: | [
1203,
1323,
1630
] | Train |
1,196 | 2 | Title: The Free Speech Phoneme Probability Estimation with Dynamic Sparsely Connected Artificial Neural Networks
Abstract: This paper presents new methods for training large neural networks for phoneme probability estimation. An architecture combining timedelay windows and recurrent connections is used to capture th... | [
840,
1038
] | Validation |
1,197 | 6 | Title: Why Does Bagging Work? A Bayesian Account and its Implications bagging's success, both in a
Abstract: The error rate of decision-tree and other classification learners can often be much reduced by bagging: learning multiple models from bootstrap samples of the database, and combining them by uniform voting. In t... | [
1053,
1290,
1484,
2634
] | Train |
1,198 | 6 | Title: Query by Committee
Abstract: We propose an algorithm called query by committee, in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement. The algorithm is studied for two toy models: the high-low game and perceptron learning o... | [
418,
517,
859,
1170,
1296,
1683
] | Test |
1,199 | 6 | Title: Query by Committee
Abstract: Tech Report 4-94 Department of Statistics, Open University, Walton Hall, MK7 6AA, UK Tech Report 205 Department of Computer Science, Monash University, Clayton, Vic. 3168, Australia Abstract: This paper examines the minimum encoding approaches to inference, Minimum Message Length (... | [
1550,
1702
] | Train |
1,200 | 2 | Title: Edges are the `Independent Components' of Natural Scenes.
Abstract: Field (1994) has suggested that neurons with line and edge selectivities found in primary visual cortex of cats and monkeys form a sparse, distributed representation of natural scenes, and Barlow (1989) has reasoned that such responses should ... | [
570,
576,
1520
] | Train |
1,201 | 3 | Title: Model Selection for Consumer Loan Application Data
Abstract: Loan applications at banks are often long, requiring the applicant to provide large amounts of data. Is all of it necessary? Can we save the applicant some frustration and the bank some expense by using only a subset of the relevant variables? To ans... | [
325,
1240
] | Test |
1,202 | 4 | Title: Between MDPs and Semi-MDPs: Learning, Planning, and Representing Knowledge at Multiple Temporal Scales
Abstract: Learning, planning, and representing knowledge at multiple levels of temporal abstraction are key challenges for AI. In this paper we develop an approach to these problems based on the mathematical ... | [
1183
] | Test |
1,203 | 2 | Title: A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice
Abstract: 190 articles about neural network learning algorithms published in 1993 and 1994 are examined for the amount of experimental evaluation they contain. 29% of them employ not even a single ... | [
542,
779,
816,
881,
1119,
1195,
1411,
1630
] | Train |
1,204 | 1 | Title: The Role of Development in Genetic Algorithms
Abstract: Technical Report Number CS94-394 Computer Science and Engineering, U.C.S.D. Abstract The developmental mechanisms transforming genotypic to phenotypic forms are typically omitted in formulations of genetic algorithms (GAs) in which these two representatio... | [
129,
537,
538,
1153,
2624
] | Test |
1,205 | 1 | Title: The Role of Development in Genetic Algorithms
Abstract: A Genetic Algorithm Tutorial Darrell Whitley Technical Report CS-93-103 (Revised) November 10, 1993 | [
163,
793,
1016,
1153
] | Train |
1,206 | 1 | Title: Learning Monitoring Strategies: A Difficult Genetic Programming Application
Abstract: Finding optimal or at least good monitoring strategies is an important consideration when designing an agent. We have applied genetic programming to this task, with mixed results. Since the agent control language was kept pur... | [
163,
789,
1544
] | Test |
1,207 | 1 | Title: Data Analyses Using Simulated Breeding and Inductive Learning Methods
Abstract: Marketing decision making tasks require the acquisition of efficient decision rules from noisy questionnaire data. Unlike popular learning-from-example methods, in such tasks, we must interpret the characteristics of the data witho... | [
163,
378,
430,
900,
1333
] | Test |
1,208 | 5 | Title: An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions
Abstract: This paper experimentally compares three approaches to program induction: inductive logic programming (ILP), genetic programming (GP), and genetic logic programming (GLP) (a variant ... | [
1429,
1434
] | Test |
1,209 | 0 | Title: S o l u t i o n Relevant A b s t r a
Abstract: Two major problems in case-based reasoning are the efficient and justified retrieval of source cases and the adaptation of retrieved solutions to the conditions of the target. For analogical theorem proving by induction, we describe how a solution-relevant abstracti... | [
539,
1215
] | Test |
1,210 | 0 | Title: Structural Similarity and Adaptation
Abstract: Most commonly, case-based reasoning is applied in domains where attribute value representations of cases are sufficient to represent the features relevant to support classification, diagnosis or design tasks. Distance functions like the Hamming-distance or their t... | [
539,
883,
1453
] | Train |
1,211 | 2 | Title: Natural Gradient Descent for Training Multi-Layer Perceptrons
Abstract: The main difficulty in implementing the natural gradient learning rule is to compute the inverse of the Fisher information matrix when the input dimension is large. We have found a new scheme to represent the Fisher information matrix. Bas... | [
1058,
1247,
1520
] | Train |
1,212 | 0 | Title: Acquiring Case Adaptation Knowledge: A Hybrid Approach
Abstract: The ability of case-based reasoning (CBR) systems to apply cases to novel situations depends on their case adaptation knowledge. However, endowing CBR systems with adequate adaptation knowledge has proven to be a very difficult task. This paper d... | [
580,
817,
818,
819,
1126,
1497,
1552
] | Train |
1,213 | 4 | Title: Learning in Multi-Robot Systems
Abstract: This paper 1 discusses why traditional reinforcement learning methods, and algorithms applied to those models, result in poor performance in dynamic, situated multi-agent domains characterized by multiple goals, noisy perception and action, and inconsistent reinforceme... | [
565,
738,
1649
] | Train |
1,214 | 0 | Title: Learning Problem-Solving Concepts by Reflecting on Problem Solving
Abstract: Learning and problem solving are intimately related: problem solving determines the knowledge requirements of the reasoner which learning must fulfill, and learning enables improved problem-solving performance. Different models of pro... | [
523,
583,
1635
] | Validation |
1,215 | 0 | Title: Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning
Abstract: Realistic and complex planning situations require a mixed-initiative planning framework in which human and automated planners interact to mutually construct a desired plan. Ideally, this joint cooperatio... | [
580,
818,
819,
824,
825,
1209,
1699,
1707
] | Test |
1,216 | 1 | Title: Evolutionary Programming and Evolution Strategies: Similarities and Differences
Abstract: Evolutionary Programming and Evolution Strategies, rather similar representatives of a class of probabilistic optimization algorithms gleaned from the model of organic evolution, are discussed and compared to each other w... | [
1035,
1299,
1571,
1719
] | Train |
1,217 | 4 | Title: A game theoretic approach to moving horizon control
Abstract: A control law is constructed for a linear time varying system by solving a two player zero sum differential game on a moving horizon, the game being that which is used to construct an H 1 controller on a finite horizon. Conditions are given under wh... | [
1349
] | Train |
1,218 | 1 | Title: Genetic algorithms with multi-parent recombination
Abstract: In this paper we investigate genetic algorithms where more than two parents are involved in the recombination operation. In particular, we introduce gene scanning as a reproduction mechanism that generalizes classical crossovers, such as n-point cros... | [
145,
163,
714,
833,
1035,
1299,
1424,
1516,
1530,
1571,
1670
] | Validation |
1,219 | 1 | Title: Putting the Genetics back into Genetic Algorithms
Abstract: In this paper we investigate genetic algorithms where more than two parents are involved in the recombination operation. In particular, we introduce gene scanning as a reproduction mechanism that generalizes classical crossovers, such as n-point cross... | [
163,
1153
] | Test |
1,220 | 2 | Title: A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets
Abstract: A novel method is proposed for combining multiple probabilistic classifiers on different feature sets. In order to achieve the improved classification performance, a generalized finite mixture ... | [
74,
1484,
1608,
1618
] | Validation |
1,221 | 1 | Title: Adapting the Evaluation Space to Improve Global Learning
Abstract: | [
910,
1797,
2703
] | Test |
1,222 | 2 | Title: Towards a General Distributed Platform for Learning and Generalization and Word Perfect Corp. 1 Introduction
Abstract: Different learning models employ different styles of generalization on novel inputs. This paper proposes the need for multiple styles of generalization to support a broad application base. The P... | [
809,
1129,
1321
] | Train |
1,223 | 6 | Title: A New Metric-Based Approach to Model Selection
Abstract: We introduce a new approach to model selection that performs better than the standard complexity-penalization and hold-out error estimation techniques in many cases. The basic idea is to exploit the intrinsic metric structure of a hypothesis space, as de... | [
848,
1335,
1422,
1607
] | Validation |
1,224 | 1 | Title: Using Real-Valued Genetic Algorithms to Evolve Rule Sets for Classification
Abstract: In this paper, we use a genetic algorithm to evolve a set of classification rules with real-valued attributes. We show how real-valued attribute ranges can be encoded with real-valued genes and present a new uniform method fo... | [
145,
163,
1333,
2673
] | Validation |
1,225 | 1 | Title: Knowledge-Based Genetic Learning
Abstract: Genetic algorithms have been proven to be a powerful tool within the area of machine learning. However, there are some classes of problems where they seem to be scarcely applicable, e.g. when the solution to a given problem consists of several parts that influence eac... | [
163,
1117,
1333
] | Train |
1,226 | 5 | Title: On Learning Multiple Descriptions of a Concept
Abstract: In sparse data environments, greater classification accuracy can be achieved by learning several concept descriptions of the data and combining their classifications. Stochastic search is a general tool which can be used to generate many good concept des... | [
1234,
1290
] | Train |
1,227 | 6 | Title: What should be minimized in a decision tree: A re-examination
Abstract: Computer Science Department University of Massachusetts at Amherst CMPSCI Technical Report 95-20 September 6, 1995 | [
1236
] | Validation |
1,228 | 4 | Title: Team-Partitioned, Opaque-Transition Reinforcement Learning
Abstract: In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of using action-dependent features to generalize the state space. In o... | [
1649,
1687,
1688,
1693
] | Train |
1,229 | 2 | Title: Using Multiple Node Types to Improve the Performance of DMP (Dynamic Multilayer Perceptron)
Abstract: This paper discusses a method for training multilayer perceptron networks called DMP2 (Dynamic Multilayer Perceptron 2). The method is based upon a divide and conquer approach which builds networks in the form... | [
809,
1615
] | Train |
1,230 | 1 | Title: Entailment for Specification Refinement
Abstract: Specification refinement is part of formal program derivation, a method by which software is directly constructed from a provably correct specification. Because program derivation is an intensive manual exercise used for critical software systems, an automated ... | [
995,
1178,
1231,
2470,
2598
] | Validation |
1,231 | 1 | Title: Type Inheritance in Strongly Typed Genetic Programming
Abstract: This paper appears as chapter 18 of Kenneth E. Kinnear, Jr. and Peter J. Angeline, editors Advances in Genetic Programming 2, MIT Press, 1996. Abstract Genetic Programming (GP) is an automatic method for generating computer programs, which are st... | [
854,
956,
995,
1178,
1230,
1232,
1495,
1985,
2086
] | Train |
1,232 | 1 | Title: Augmenting Collective Adaptation with Simple Process Agents
Abstract: We have integrated the distributed search of genetic programming based systems with collective memory to form a collective adaptation search method. Such a system significantly improves search as problem complexity is increased. However, the... | [
995,
1178,
1231,
2211,
2598
] | Train |
1,233 | 0 | Title: A CBR Integration From Inception to Productization
Abstract: Our case-based reasoning (CBR) integration with the constraint satisfaction problem (CSP) formalism has undergone several transformations on its journey from initial research idea to product-intent design. Both unexpected research results as well as ... | [
922,
923
] | Validation |
1,234 | 5 | Title: Concept Learning and the Problem of Small
Abstract: | [
13,
790,
937,
977,
1187,
1226,
1263,
1275,
1510
] | Train |
1,235 | 6 | Title: Unbiased Assessment of Learning Algorithms
Abstract: In order to rank the performance of machine learning algorithms, many researchers conduct experiments on benchmark data sets. Since most learning algorithms have domain-specific parameters, it is a popular custom to adapt these parameters to obtain a minimal... | [
1512,
2508
] | Train |
1,236 | 6 | Title: Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction
Abstract: We report on a series of experiments in which all decision trees consistent with the training data are constructed. These experiments were run to gain an understanding of the properties of the set of... | [
296,
382,
861,
1227,
1669
] | Train |
1,237 | 6 | Title: An Empirical Evaluation of Bagging and Boosting
Abstract: An ensemble consists of a set of independently trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurat... | [
826,
1422,
1457,
1484,
1521
] | Train |
1,238 | 6 | Title: On Pruning and Averaging Decision Trees
Abstract: Pruning a decision tree is considered by some researchers to be the most important part of tree building in noisy domains. While, there are many approaches to pruning, an alternative approach of averaging over decision trees has not received as much attention. ... | [
378,
1025,
1290,
1500,
1550
] | Validation |
1,239 | 2 | Title: Hidden Markov Modeling of simultaneously recorded cells in the Associative cortex of behaving monkeys
Abstract: A widely held idea regarding information processing in the brain is the cell-assembly hypothesis suggested by Hebb in 1949. According to this hypothesis, the basic unit of information processing in t... | [
1387
] | Train |
1,240 | 3 | Title: Model Selection and Accounting for Model Uncertainty in Linear Regression Models
Abstract: 1 Adrian E. Raftery is Professor of Statistics and Sociology, David Madigan is Assistant Professor of Statistics, and Jennifer Hoeting is a Ph.D. Candidate, all at the Department of Statistics, GN-22, University of Washi... | [
84,
742,
772,
841,
897,
912,
950,
987,
998,
999,
1086,
1141,
1147,
1201,
1241,
1347,
1527
] | Train |
1,241 | 3 | Title: Bayesian Graphical Models for Discrete Data
Abstract: z York's research was supported by a NSF graduate fellowship. The authors are grateful to Julian Besag, David Bradshaw, Jeff Bradshaw, James Carlsen, David Draper, Ivar Heuch, Robert Kass, Augustine Kong, Steffen Lauritzen, Adrian Raftery, and James Zidek f... | [
84,
772,
912,
950,
998,
1141,
1147,
1240,
1347,
2559
] | Test |
1,242 | 2 | Title: Categorical Perception in Facial Emotion Classification
Abstract: We present an automated emotion recognition system that is capable of identifying six basic emotions (happy, surprise, sad, angry, fear, disgust) in novel face images. An ensemble of simple feed-forward neural networks are used to rate each of t... | [
939
] | Train |
1,243 | 2 | Title: BLIND SEPARATION OF REAL WORLD AUDIO SIGNALS USING OVERDETERMINED MIXTURES
Abstract: We discuss the advantages of using overdetermined mixtures to improve upon blind source separation algorithms that are designed to extract sound sources from acoustic mixtures. A study of the nature of room impulse responses h... | [
570,
1245,
1524
] | Test |
1,244 | 5 | Title: Producing More Comprehensible Models While Retaining Their Performance
Abstract: Rissanen's Minimum Description Length (MDL) principle is adapted to handle continuous attributes in the Inductive Logic Programming setting. Application of the developed coding as a MDL pruning mechanism is devised. The behavior o... | [
314,
344,
348,
1061,
1596
] | Train |
1,245 | 2 | Title: Blind separation of delayed and convolved sources.
Abstract: We address the difficult problem of separating multiple speakers with multiple microphones in a real room. We combine the work of Torkkola and Amari, Cichocki and Yang, to give Natural Gradient information maximisation rules for recurrent (IIR) netwo... | [
570,
576,
1243,
1520,
1524
] | Train |
1,246 | 2 | Title: NIPS*97 Multiplicative Updating Rule for Blind Separation Derived from the Method of Scoring
Abstract: For blind source separation, when the Fisher information matrix is used as the Riemannian metric tensor for the parameter space, the steepest descent algorithm to maximize the likelihood function in this Riem... | [
570,
1520
] | Train |
1,247 | 2 | Title: NIPS*97 The Efficiency and The Robustness of Natural Gradient Descent Learning Rule
Abstract: We have discovered a new scheme to represent the Fisher information matrix of a stochastic multi-layer perceptron. Based on this scheme, we have designed an algorithm to compute the inverse of the Fisher information m... | [
1211
] | Test |
1,248 | 0 | Title: Lazy Acquisition of Place Knowledge
Abstract: In this paper we define the task of place learning and describe one approach to this problem. Our framework represents distinct places as evidence grids, a probabilistic description of occupancy. Place recognition relies on nearest neighbor classification, augmente... | [
66,
688,
835
] | Train |
1,249 | 1 | Title: Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces
Abstract: Let us call a non-deterministic incremental algorithm one that is able to construct any solution to a combinatorial problem by selecting incrementally an ordered sequence of choices that defines this so... | [
793,
1054,
1408,
1473,
1474,
1728,
1734
] | Test |
1,250 | 2 | Title: Priming, Perceptual Reversal, and Circular Reaction in a Neural Network Model of Schema-Based Vision
Abstract: VISOR is a neural network system for object recognition and scene analysis that learns visual schemas from examples. Processing in VISOR is based on cooperation, competition, and parallel bottom-up an... | [
399,
1251
] | Test |
1,251 | 2 | Title: VISOR: Schema-based Scene Analysis with Structured Neural Networks
Abstract: A novel approach to object recognition and scene analysis based on neural network representation of visual schemas is described. Given an input scene, the VISOR system focuses attention successively at each component, and the schema r... | [
399,
1250
] | Test |
1,252 | 2 | Title: Constructive Training Methods for Feedforward Neural Networks with Binary Weights
Abstract: DIMACS Technical Report 95-35 August 1995 | [
820,
829,
830,
907,
918,
1115,
1477,
1485,
1634
] | Train |
1,253 | 4 | Title: USING A GENETIC ALGORITHM TO LEARN BEHAVIORS FOR AUTONOMOUS VEHICLES
Abstract: Truly autonomous vehicles will require both projec - tive planning and reactive components in order to perform robustly. Projective components are needed for long-term planning and replanning where explicit reasoning about future st... | [
163,
811,
910,
965,
1131,
1311
] | Test |
1,254 | 2 | Title: BACKPROPAGATION SEPARATES WHERE PERCEPTRONS DO
Abstract: Feedforward nets with sigmoidal activation functions are often designed by minimizing a cost criterion. It has been pointed out before that this technique may be outperformed by the classical perceptron learning rule, at least on some problems. In this p... | [
930,
1062,
1464
] | Train |
1,255 | 3 | Title: Modelling Risk from a Disease in Time and Space
Abstract: This paper combines existing models for longitudinal and spatial data in a hierarchical Bayesian framework, with particular emphasis on the role of time- and space-varying covariate effects. Data analysis is implemented via Markov chain Monte Carlo meth... | [
95,
99,
358,
894
] | Train |
1,256 | 5 | Title: A BENCHMARK FOR CLASSIFIER LEARNING
Abstract: Technical Report 474 November 1993 | [
881,
1019,
1644,
2675
] | Train |
1,257 | 1 | Title: The Schema Theorem and Price's Theorem
Abstract: Holland's Schema Theorem is widely taken to be the foundation for explanations of the power of genetic algorithms (GAs). Yet some dissent has been expressed as to its implications. Here, dissenting arguments are reviewed and elaborated upon, explaining why the S... | [
163,
380,
1153,
1719,
1872,
2087,
2175,
2259
] | Train |
1,258 | 2 | Title: Independent Component Analysis of Simulated EEG Using a Three-Shell Spherical Head Model 1
Abstract: 1 This report was supported in part by the Navy Medical Research and Development Command and the Office of Naval Research, Department of the Navy under work unit ONR.Reimb-6429. The views expressed in this arti... | [
570,
576,
1520
] | Train |
1,259 | 5 | Title: Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning
Abstract: An approach to analytic learning is described that searches for accurate entailments of a Horn Clause domain theory. A hill-climbing search, guided by an information based evaluation function, is performed by applying a... | [
92,
136,
521,
893,
1081,
1082,
1944,
2213,
2312
] | Train |
1,260 | 6 | Title: Transferring and Retraining Learned Information Filters
Abstract: Any system that learns how to filter documents will suffer poor performance during an initial training phase. One way of addressing this problem is to exploit filters learned by other users in a collaborative fashion. We investigate "direct tran... | [
344,
654,
1104,
1269,
1312,
2090
] | Validation |
1,261 | 1 | Title: EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES
Abstract: We present a coevolutionary architecture for solving decomposable problems and apply it to the evolution of artificial neural networks. Although this work is preliminary in nature it has a number of advantages over non-coevolutionary approaches. The... | [
247,
1114,
1117,
2089
] | Train |
1,262 | 2 | Title: Maximum A Posteriori Classification of DNA Structure from Sequence Information
Abstract: We introduce an algorithm, lllama, which combines simple pattern recognizers into a general method for estimating the entropy of a sequence. Each pattern recognizer exploits a partial match between subsequences to build a ... | [
1104
] | Validation |
1,263 | 0 | Title: Using Partitioning to Speed Up Specific-to-General Rule Induction
Abstract: RISE (Domingos 1995; in press) is a rule induction algorithm that proceeds by gradually generalizing rules, starting with one rule per example. This has several advantages compared to the more common strategy of gradually specializing ... | [
1234,
2585
] | Train |
1,264 | 1 | Title: An Artificial Life Model for Investigating the Evolution of Modularity
Abstract: To investigate the issue of how modularity emerges in nature, we present an Artificial Life model that allow us to reproduce on the computer both the organisms (i.e., robots that have a genotype, a nervous system, and sensory and ... | [
1134,
1738
] | Train |
1,265 | 2 | Title: Differential theory of learning for efficient neural network pattern recognition
Abstract: We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationshi... | [
611,
921,
1725
] | Train |
1,266 | 2 | Title: A Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks
Abstract: With most machine learning methods, if the given knowledge representation space is inadequate then the learning process will fail. This is also true with methods using neural networks as the form of the representation sp... | [
836,
1301,
1576,
1663
] | Train |
1,267 | 6 | Title: Estimating the Accuracy of Learned Concepts
Abstract: This paper investigates alternative estimators of the accuracy of concepts learned from examples. In particular, the cross-validation and 632 bootstrap estimators are studied, using synthetic training data and the foil learning algorithm. Our experimental r... | [
344,
1335,
1500,
1512
] | Train |
1,268 | 3 | Title: The BATmobile: Towards a Bayesian Automated Taxi
Abstract: The problem of driving an autonomous vehicle in highway traffic engages many areas of AI research and has substantial economic significance. We describe work in progress on a new approach to this problem based on a decision-theoretic architecture using... | [
788,
976,
1186,
1414,
1757,
1842,
1898,
2140,
2323,
2419
] | Validation |
1,269 | 6 | Title: Context-sensitive learning methods for text categorization
Abstract: Two recently implemented machine learning algorithms, RIPPER and sleeping experts for phrases, are evaluated on a number of large text categorization problems. These algorithms both construct classifiers that allow the "context" of a word w t... | [
418,
453,
569,
876,
1260,
1312,
2618
] | Validation |
1,270 | 6 | Title: Automatic Parameter Selection by Minimizing Estimated Error
Abstract: We address the problem of finding the parameter settings that will result in optimal performance of a given learning algorithm using a particular dataset as training data. We describe a "wrapper" method, considering determination of the best... | [
208,
236,
430,
627,
1335,
2342
] | Train |
1,271 | 5 | Title: Beyond Correlation: Bringing Artificial Intelligence to Events Data
Abstract: The Feature Vector Editor offers a user-extensible environment for exploratory data analysis. Several empirical studies have applied this environment to the SHER-FACS International Conflict Management dataset. Current analysis techni... | [
236,
997
] | Train |
1,272 | 2 | Title: Input-Output Analysis of Feedback Loops with Saturation Nonlinearities
Abstract: The Feature Vector Editor offers a user-extensible environment for exploratory data analysis. Several empirical studies have applied this environment to the SHER-FACS International Conflict Management dataset. Current analysis tec... | [
1281,
1282,
1346,
1451,
1604
] | Test |
1,273 | 6 | Title: The Sources of Increased Accuracy for Two Proposed Boosting Algorithms
Abstract: We introduce two boosting algorithms that aim to increase the generalization accuracy of a given classifier by incorporating it as a level-0 component in a stacked generalizer. Both algorithms construct a complementary level-0 cla... | [
319,
569,
686,
826,
1422
] | Train |
1,274 | 1 | Title: Surgery
Abstract: Object localization has applications in many areas of engineering and science. The goal is to spatially locate an arbitrarily-shaped object. In many applications, it is desirable to minimize the number of measurements collected for this purpose, while ensuring sufficient localization accuracy... | [
163,
343,
427,
1303,
1305
] | Validation |
1,275 | 5 | Title: Fossil: A Robust Relational Learner
Abstract: The research reported in this paper describes Fossil, an ILP system that uses a search heuristic based on statistical correlation. This algorithm implements a new method for learning useful concepts in the presence of noise. In contrast to Foil's stopping criterion... | [
344,
378,
426,
585,
1234,
2290,
2291,
2617
] | Train |
1,276 | 3 | Title: Heuristics and Normative Models of Judgment under Uncertainty
Abstract: Psychological evidence shows that probability theory is not a proper descriptive model of intuitive human judgment. Instead, some heuristics have been proposed as such a descriptive model. This paper argues that probability theory has limi... | [
1503,
1504,
1506,
1525
] | Validation |
1,277 | 1 | Title: Evolution of Pseudo-colouring Algorithms for Image Enhancement with Interactive Genetic Programming
Abstract: Technical Report: CSRP-97-5 School of Computer Science The University of Birmingham Abstract In this paper we present an approach to the interactive development of programs for image enhancement with G... | [
163,
1476,
1533,
2152,
2277,
2470
] | Train |
1,278 | 0 | Title: A Functional Theory of Creative Reading
Abstract: Reading is an area of human cognition which has been studied for decades by psychologists, education researchers, and artificial intelligence researchers. Yet, there still does not exist a theory which accurately describes the complete process. We believe that ... | [
289,
486,
583,
1534
] | Test |
1,279 | 1 | Title: Speeding up Genetic Programming: A Parallel BSP implementation the Bulk Synchronous Parallel Pro gramming (BSP)
Abstract: | [
1065
] | Train |
1,280 | 6 | Title: Theory and Practice of Vector Quantizers Trained on Small Training Sets
Abstract: We examine how the performance of a memoryless vector quantizer changes as a function of its training set size. Specifically, we study how well the training set distortion predicts test distortion when the training set is a rando... | [
955
] | Train |
1,281 | 2 | Title: On Finite Gain Stabilizability of Linear Systems Subject to Input Saturation
Abstract: This paper deals with (global) finite-gain input/output stabilization of linear systems with saturated controls. For neutrally stable systems, it is shown that the linear feedback law suggested by the passivity approach inde... | [
948,
1272,
1282,
1346,
1451,
1471,
1604
] | Validation |
1,282 | 2 | Title: Global Stabilization of Linear Discrete-Time Systems with Bounded Feedback
Abstract: This paper deals with the problem of global stabilization of linear discrete time systems by means of bounded feedback laws. The main result proved is an analog of one proved for the continuous time case by the authors, and sh... | [
948,
1022,
1272,
1281,
1446,
1471,
1494
] | Train |
1,283 | 2 | Title: Bilinear Separation of Two Sets in n-Space
Abstract: The NP-complete problem of determining whether two disjoint point sets in the n-dimensional real space R n can be separated by two planes is cast as a bilinear program, that is minimizing the scalar product of two linear functions on a polyhedral set. The bi... | [
142,
230,
391,
427,
823,
1284,
1318,
1547
] | Train |
1,284 | 2 | Title: Feature Selection via Mathematical Programming
Abstract: The problem of discriminating between two finite point sets in n-dimensional feature space by a separating plane that utilizes as few of the features as possible, is formulated as a mathematical program with a parametric objective function and linear con... | [
230,
427,
430,
1055,
1169,
1283
] | Train |
1,285 | 2 | Title: Learning Context-free Grammars: Capabilities and Limitations of a Recurrent Neural Network with an External Stack Memory
Abstract: This work describes an approach for inferring Deterministic Context-free (DCF) Grammars in a Connectionist paradigm using a Recurrent Neural Network Pushdown Automaton (NNPDA). The... | [
405,
770,
968,
1176,
1298,
1382
] | Train |
1,286 | 1 | Title: HGA: A Hardware-Based Genetic Algorithm
Abstract: A genetic algorithm (GA) is a robust problem-solving method based on natural selection. Hardware's speed advantage and its ability to parallelize offer great rewards to genetic al gorithms. Speedups of 1-3 orders of magnitude have been observed when frequently ... | [
163,
1136
] | Validation |
1,287 | 3 | Title: Factorial Hidden Markov Models
Abstract: One of the basic probabilistic tools used for time series modeling is the hidden Markov model (HMM). In an HMM, information about the past of the time series is conveyed through a single discrete variable|the hidden state. We present a generalization of HMMs in which th... | [
499,
787,
810,
905,
945,
976,
1397,
1414,
1437
] | Train |
1,288 | 3 | Title: Exploiting Tractable Substructures in Intractable Networks
Abstract: We develop a refined mean field approximation for inference and learning in probabilistic neural networks. Our mean field theory, unlike most, does not assume that the units behave as independent degrees of freedom; instead, it exploits in a ... | [
107,
108,
499,
787,
1091,
1128,
1414,
1461,
1502,
1593
] | Validation |
1,289 | 2 | Title: 5 Bayesian estimation 5.1 Introduction
Abstract: This chapter takes a different standpoint to address the problem of learning. We will here reason only in terms of probability, and make extensive use of the chain rule known as "Bayes' rule". A fast definition of the basics in probability is provided in appendi... | [
157,
1452
] | Test |
1,290 | 6 | Title: A THEORY OF LEARNING CLASSIFICATION RULES
Abstract: This chapter takes a different standpoint to address the problem of learning. We will here reason only in terms of probability, and make extensive use of the chain rule known as "Bayes' rule". A fast definition of the basics in probability is provided in appe... | [
218,
378,
423,
429,
893,
1019,
1025,
1191,
1197,
1226,
1238,
1644,
1712,
1918,
2080,
2169,
2329
] | Validation |
1,291 | 3 | Title: Bits-back coding software guide
Abstract: Abstract | In this document, I first review the theory behind bits-back coding (aka. free energy coding) (Frey and Hinton 1996) and then describe the interface to C-language software that can be used for bits-back coding. This method is a new approach to the problem of... | [
1548
] | Validation |
1,292 | 5 | Title: CONSTRUCTIVE INDUCTION FROM DATA IN AQ17-DCI: Further Experiments
Abstract: Abstract | In this document, I first review the theory behind bits-back coding (aka. free energy coding) (Frey and Hinton 1996) and then describe the interface to C-language software that can be used for bits-back coding. This method i... | [
960,
1049,
1071,
1085
] | Train |
1,293 | 2 | Title: Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks
Abstract: A modified Recurrent Neural Network (RNN) is used to learn a Self-Routing Interconnection Network (SRIN) from a set of routing examples. The RNN is modified so that it has several distinct initial states. This is equiv... | [
672,
1592,
1600,
1606,
2284
] | Train |
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