Learning relations from noisy examples : an empirical comparison of LINUS and FOIL

Content: 
Front Matter, Page i
Copyright, Page ii
Preface, Page iii, Larry Birnbaum, Gregg Collins
ML91 Organizing Committee, Page iv
ML91 Workshop Committees, Page v
Design Rationale Capture as Knowledge Acquisition: Tradeoffs in the Design of Interactive Tools, Pages 3-12, Thomas Gruber, John Boose, Catherine Baudin, Jay Weber
A Domain-Independent Framework for Effective Experimentation in Planning, Pages 13-17, Yolanda Gil
Knowledge Refinement Using a High-Level, Non-Technical Vocabulary, Pages 18-22, Eric K. Jones
Improving the Performance of Inconsistent Knowledge Bases via Combined Optimization Method, Pages 23-27, Yong Ma, David C. Wilkins
The Flexibility of Speculative Refinement, Pages 28-32, Susan Craw, D. Sleeman
Generating Error Candidates for Assigning Blame in a Knowledge Base, Pages 33-37, Michael Weintraub, Tom Bylander
A Prototype Based Symbolic Concept Learning System, Pages 41-45, Michael de la Maza
Combining Evidence of Deep and Surface Similarity, Pages 46-50, Doug Fisher, Jungsoon Yoo
The Importance of Causal Structure and Facts in Evaluating Explanations, Pages 51-54, Mary Gick, Stan Matwin
Learning Words from Context, Pages 55-59, Peter M. Hastings, Steven L. Lytinen, Robert K. Lindsay
Modeling the Acquisition and Improvement of Motor Skills, Pages 60-64, Wayne Iba
A Computational Model of Acquisition for Children's Addition Strategies, Pages 65-69, Randolph M. Jones, Kurt VanLehn
Internal world models and supervised learning, Pages 70-74, Michael I. Jordan, David E. Rumelhart
Babel: A Psychologically Plausible Cross-Linguistic Model of Lexical and Syntactic Acquisition, Pages 75-79, Rick Kazman
The Acquisition of Human Planning Expertise, Pages 80-84, Pat Langley, John A. Allen
Adaptive Pattern-Oriented Chess, Pages 85-89, Robert Levinson, Richard Snyder
Variability Bias and Category Learning, Pages 90-94, Joel D. Martin, Dorrit O. Billman
A Constraint-Motivated Model of Lexical Acquisition, Pages 95-99, Craig S. Miller, John E. Laird
Computer Modelling of Acquisition Orders in Child Language, Pages 100-104, Sheldon Nicholl, David C. Wilkins
Simulating Stages of Human Cognitive Development with Connectionist Models, Pages 105-109, Thomas R. Shultz
Learning Physics Via Explanation-based Learning of Correctness and Analogical Search Control, Pages 110-114, Kurt VanLehn, Randolph M. Jones
Incremental Constructive Induction: An Instance-Based Approach, Pages 117-121, David W. Aha
A Transformational Approach to Constructive Induction, Pages 122-126, James P. Call, Paul E. Utgoff
Learning Variable Descriptors for Applying Heuristics Across CSP Problems, Pages 127-131, David S. Day
Informed Pruning in Constructive Induction, Pages 132-136, George Drastal
A Hybrid Method for Feature Generation, Pages 137-141, Tom E. Fawcett, Paul E. Utgoff
Abstracting Concepts with Inverse Resolution, Pages 142-146, A. Giordana, L. Saitta, D. Roverso
Opportunistic Constructive Induction: Using Fragments of Domain Knowledge to Guide Construction, Pages 147-152, Gregg H. Gunsch, Larry A. Rendell
Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning, Pages 153-157, Carl Myers Kadie
Discovering Production Rules with Higher Order Neural Networks: a Case Study, Pages 158-162, Adam Kowalczyk, Herman L. Ferrá, Ken Gardiner
Constructive Induction on Symbolic Features: Introducing New Comparative Terms, Pages 163-167, Bing Leng, Bruce G. Buchanan
A Critical Comparison of Various Methods Based on Inverse Resolution, Pages 168-172, Xiaofeng (Charles) Ling, Malur Aji Narayan
The Need for Constructive Induction, Pages 173-177, Christopher J. Matheus
Constructive Induction in Theory Refinement, Pages 178-182, Raymond J. Mooney, Dirk Ourston
ID2-of-3: Constructive Induction of M-of-N Concepts for Discriminators in Decision Trees, Pages 183-187, Patrick M. Murphy, Michael J. Pazzani
Relations, Knowledge and Empirical Learning, Pages 188-192, Harish Ragavan, Larry Rendell
Learning Concepts by Synthesizing Minimal Threshold Gate Networks, Pages 193-197, Arlindo L. Oliveira, Alberto Sangiovanni-Vincentelli
On the Effect of Instance Representation on Generalization, Pages 198-202, Sharad Saxena
Relational clichés: Constraining constructive induction during relational learning, Pages 203-207, Glenn Silverstein, Michael J. Pazzani
Learning Polynomial Functions by Feature Construction, Pages 208-212, Richard S. Sutton, Christopher J. Matheus
Constructive Induction in Knowledge-Based Neural Networks, Pages 213-217, Geoffrey G. Towell, Mark W. Craven, Jude W. Shavlik
Feature Construction in Structural Decision Trees, Pages 218-222, Larry Watanabe, Larry Rendell
Fringe-Like Feature Construction: A Comparative Study and a Unifying Scheme, Pages 223-227, Der-Shung Yang, Larry Rendell, Gunnar Blix
A Neural Network Approach to Constructive Induction, Pages 228-232, Dit-Yan Yeung
Learning in Intelligent Information Retrieval, Pages 235-239, David D. Lewis
A Probabilistic Retrieval Scheme for Cluster-based Adaptive Information Retrieval, Pages 240-244, Jay N. Bhuyan, Vijay V. Raghavan
Classification Trees for Information Retrieval, Pages 245-249, Stuart L. Crawford, Robert M. Fung, Lee A. Appelbaum, Richard M. Tong
Query Formulation through Knowledge Acquisition, Pages 250-254, Sanjiv K. Bhatia, Jitender S. Deogun, Vijay V. Raghavan
Incremental Learning in a Probabilistic Information Retrieval System, Pages 255-259, A. Goker, T.L. McCluskey
Query Learning Using an ANN with Adaptive Architecture, Pages 260-264, K.L. Kwok
A Goal-Based Approach to Intelligent Information Retrieval, Pages 265-269, Ashwin Ram, Lawrence Hunter
Machine Learning in the Combination of Expert Opinion Approach to IR, Pages 270-274, Paul Thompson
PREDICTING ACTIONS FROM INDUCTION ON PAST PERFORMANCE, Pages 275-279, Steven Walczak
Decision-Theoretic Learning in an Action System, Pages 283-287, Matthew Brand
On Becoming Decreasingly Reactive: Learning to Deliberate Minimally, Pages 288-292, Steve A. Chien, Melinda T. Gervasio, Gerald F. DeJong
Learning the Persistence of Actions in Reactive Control Rules, Pages 293-297, Helen G. Cobb, John J. Grefenstette
Learning to Avoid Obstacles through Reinforcement, Pages 298-302, José del R. Millan, Carme Torras
Learning Footfall Evaluation for a Walking Robot, Pages 303-307, Goang-Tay Hsu, Reid Simmons
The Blind Leading the Blind: Mutual Refinement of Approximate Theories, Pages 308-312, Smadar T. Kedar, John L. Bresina, C. Lisa Dent
Learning to Select a Model in a Changing World, Pages 313-317, Mieczyslaw M. Kokar, Spiridon A. Reveliotis
Learning from Deliberated Reactivity, Pages 318-322, Bruce Krulwich
Self-improvement Based On Reinforcement Learning, Planning and Teaching, Pages 323-327, Long-Ji Lin
Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption Architecture, Pages 328-332, Sridhar Mahadevan, Jonathan Connell
Variable Resolution Dynamic Programming: Efficiently Learning Action Maps in Multivariate Real-valued State-spaces, Pages 333-337, Andrew W. Moore
Learning a Set of Primitive Actions with an Uninterpreted Sensorimotor Apparatus, Pages 338-342, David Pierce
Incremental Development of Complex Behaviors through Automatic Construction of Sensory-motor Hierarchies, Pages 343-347, Mark Ring
Transfer of Learning Across Compositions of Sequential Tasks, Pages 348-352, Satinder P. Singh
Planning by Incremental Dynamic Programming, Pages 353-357, Richard S. Sutton
Learning a Cost-Sensitive Internal Representation for Reinforcement Learning, Pages 358-362, Ming Tan
Complexity and Cooperation in Q-Learning, Pages 363-367, Steven D. Whitehead
Scaling Reinforcement Learning Techniques via Modularity, Pages 368-372, Lambert E. Wixson
Probabilistic Concept Formation in Relational Domains, Pages 375-379, John A. Allen, Kevin Thompson
Experiments in non-monotonic learning, Pages 380-384, Michael Bain
Learning Qualitative Models of Dynamic Systems, Pages 385-388, Ivan Bratko, Stephen Muggleton, Alen Varšek
An Investigation of Noise-Tolerant Relational Concept Learning Algorithms, Pages 389-393, Clifford A. Brunk, Michael J. Pazzani
Integrity Constraints and Interactive Concept-Learning, Pages 394-398, Luc De Raedt, Maurice Bruynooghe, Bern Martens
Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL, Pages 399-402, Sašo Džeroski, Nada Lavrač
Inducing Temporal Fault Diagnostic Rules from a Qualitative Model, Pages 403-406, C. Feng
Learning Spatial Relations from Images, Pages 407-411, Kazuo Hiraki, John Gennari, Yoshinobu Yamamoto, Yuichiro Anzai
Using Inverse Resolution to Learn Relations from Experiments, Pages 412-416, David Hume, Claude Sammut
Efficient Learning of Logic Programs with Non-determinate, Non-discriminating Literals, Pages 417-421, Boonserm Kijsirikul, Masayuki Numao, Masamichi Shimura
Learning Search Control Rules for Planning: An Inductive Approach, Pages 422-426, Christopher Leckie, Ingrid Zukerman
Learning Constrained Atoms, Pages 427-431, C. David Page, Alan M. Prisch
A knowledge-intensive approach to learning relational concepts, Pages 432-436, Michael J. Pazzani, Clifford A. Brunk, Glenn Silverstein
THE CONSISTENT CONCEPT AXIOM, Pages 437-441, Zhaogang Qian, Keki B. Irani
Determinate Literals in Inductive Logic Programming, Pages 442-446, J.R. Quinlan
First-Order Theory Revision, Pages 447-451, Bradley L. Richards, Raymond J. Mooney
Completeness for inductive procedures, Pages 452-456, Celine ROUVEIROL
Constraints on Predicate Invention, Pages 457-461, Ruediger Wirth, Paul O'Rorke
Revising Relational Domain Theories, Pages 462-466, James Wogulis
Learning Stochastic Motifs from Genetic Sequences, Pages 467-471, Kenji Yamanishi, Akihiko Konagaya
Refinement of Approximate Reasoning-based Controllers by Reinforcement Learning, Pages 475-479, Hamid R. Berenji, Sterling Software
Improving Learning Using Causality and Abduction, Pages 480-484, M. Botta, S. Ravotto, L. Saitta, S.B. Sperotto
The DUCTOR: A Theory Revision System for Propositional Domains, Pages 485-489, Timothy Cain
The Generality Of Overgenerality, Pages 490-494, William W. Cohen
Probabilistic Evaluation of Bias for Learning Systems, Pages 495-499, Marie desJardins
Incremental Refinement of Approximate Domain Theories, Pages 500-504, Ronen Feldman, Alberto Segre, Moshe Koppel
AN ENHANCER FOR REACTIVE PLANS, Pages 505-508, Diana F. Gordon
A Hybrid Approach to Guaranteed Effective Control Strategies, Pages 509-513, Jonathan Gratch, Gerald Dejong
Revision Cost for Theory Refinement, Pages 514-518, Rei Hamakawa
Revision of Reduced Theories, Pages 519-523, Xiaofeng (Charles) Ling, Marco Valtorta
Refining Domain Theories Expressed as Finite-State Automata, Pages 524-528, Richard Maclin, Jude W. Shavlik
A SMALLEST GENERALIZATION STEP STRATEGY, Pages 529-533, Claire NEDELLEC
Improving Shared Rules in Multiple Category Domain Theories, Pages 534-538, Dirk Ourston, Raymond J. Mooney
Discovering Regularities from Large Knowledge Bases, Pages 539-543, Wei-Min Shen
Learning with Inscrutable Theories, Pages 544-548, Prasad Tadepalli
A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications, Pages 549-553, Gheorghe D. Tecuci, Ryszard S. Michalski
Using Background Knowledge in Concept Formation, Pages 554-558, Kevin Thompson, Pat Langley, Wayne Iba
A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems, Pages 559-563, Bradley L. Whitehall, Stephen C-Y. Lu
Is it a Pocket or a Purse? Tightly Coupled Theory and Data Driven Learning, Pages 564-568, Edward J. Wisniewski, Douglas L. Medin
Identifying Cost Effective Boundaries of Operationality, Pages 569-573, Jungsoon Yoo, Doug Fisher
Machine Learning In Engineering Automation, Pages 577-580, Steve Chien, Bradley Whitehall, Thomas Dietterich, Richard Doyle, Brian Falkenhainer, James Garrett, Stephen Lut
Noise-Resistant Classification: Subsymbolic and Hybrid Architectures for Event Classification in Plasma Physics, Pages 581-585, Leonid V. Belyaev, Loretta P. Falcone
Comparing Stochastic Planning to the Acquisition of Increasingly Permissive Plans for Complex, Uncertain Domains, Pages 586-590, Scott Bennett, Gerald Dejong
Conceptual Clustering and Exploratory Data Analysis, Pages 591-595, Gautam Biswas, Jerry Weinberg, Qun Yang, Glenn R. Roller
Mega induction: a Test Flight, Pages 596-599, Jason Catlett
Knowledge Compilation to Speed Up Numerical Optimization, Pages 600-604, Giuseppe Cerbone, Thomas G. Dietterich
Model Revision: A Theory of Incremental Model Learning, Pages 605-609, Ashok K. Goel
Learning Analytical Knowledge about VLSI-Design from Observation, Pages 610-614, Jurgen Herrmann
Continuous Conceptual Set Covering: Learning Robot Operators From Examples, Pages 615-619, Carl Myers Kadie
Machine Learning for Nondestructive Evaluation, Pages 620-624, Paul O'Rorke, Steven Morris, Michael Amirfathi, William Bond, Daniel St. Clair
Improving Recognition Effectiveness of Noisy Texture Concepts through Optimization of Their Descriptions, Pages 625-629, Peter W. Pachowicz, Jerzy W. Bala
Knowledge-Based Equation Discovery in Engineering Domains, Pages 630-634, R. Bharat Rao, Stephen C-Y. Lu, Robert E. Stepp
Designing integrated learning systems for engineering design, Pages 635-639, Yoram Reich
Database Consistency via Inductive Learning, Pages 640-644, Jeffrey C. Schlimmer
AIMS: An Adaptive Interactive Modeling System for Supporting Engineering Decision Making, Pages 645-649, David K. Tcheng, Bruce L. Lambert, Stephen C-Y. Lu, Larry A. Rendell
Decision Tree Induction of 3-D Manufacturing Features, Pages 650-654, Larry Watanabe, Sudhakar Yerramareddy
Knowledge Acquisition Combining Analytical and Empirical Techniques, Pages 657-661, Mario Martin, Ramon Sangüesa, Ulises Cortes
AUTOMATED KNOWLEDGE ACQUISITION, Pages 663-667
Author Index, Pages 669-671