8. ML 1991
Lawrence Birnbaum,
Gregg Collins (Eds.):
Proceedings of the Eighth International Workshop (ML91). Morgan Kaufmann,
ISBN 1-55860-200-3
Automated Knowledge Acquisition
Computational Models of Human Learning
- Michael de la Maza:
A Prototype Based Symbolic Concept Learning System.
41-45
- Douglas H. Fisher, Jungsoon P. Yoo:
Combining Evidence of Deep and Surface Similarity.
46-50
- Mary Gick, Stan Matwin:
The Importance of Causal Structure and Facts in Evaluating Explanations.
51-54
- Peter M. Hastings, Steven L. Lytinen, Robert K. Lindsay:
Learning Words From Context.
55-59
- Wayne Iba:
Modeling the Acquisition and Improvement of Motor Skkills.
60-64
- Randolph M. Jones, Kurt VanLehn:
A Computational Model of Acquisition for Children's Addtion Strategies.
65-69
- Michael I. Jordan, David E. Rumelhart:
Internal World Models and Supervised Learning.
70-74
- Rick Kazman:
Babel: A Psychologically Plausible Cross-Linguistic Model of Lexical and Syntactic Acquisition.
75-79
- Pat Langley, John A. Allen:
The Acquisition of Human Planning Expertise.
80-84
- Robert Levinson, Richard Snyder:
Adaptive Pattern-Oriented Chess.
85-89
- Joel D. Martin, Dorrit Billman:
Variability Bias and Category Learning.
90-94
- Craig S. Miller, John E. Laird:
A Constraint-Motivated Model of Lexical Acquisition.
95-99
- Sheldon Nicholl, David C. Wilkins:
Computer Modelling of Acquisition Orders in Child Language.
100-104
- Thomas R. Shultz:
Simulating Stages of Human Cognitive Development With Connectionist Models.
105-109
- Kurt VanLehn, Randolph M. Jones:
Learning Physics Via Explanation-Based Learning of Correctness and Analogical Search Control.
110-114
Constructive Induction
- David W. Aha:
Incremental Constructive Induction: An Instance-Based Approach.
117-121
- James P. Callan, Paul E. Utgoff:
A Transformational Approach to Constructive Induction.
122-126
- David S. Day:
Learning Variable Descriptors for Applying Heuristics Across CSP Problems.
127-131
- George Drastal:
Informed Pruning in Constructive Induction.
132-136
- Tom Fawcett, Paul E. Utgoff:
A Hybrid Method for Feature Generation.
137-141
- Attilio Giordana, Lorenza Saitta, Davide Roverso:
Abstracting Concepts with Inverse Resolution.
142-146
- Gregg H. Gunsch, Larry A. Rendell:
Opportunistic Constructive Induction.
147-152
- Carl Myers Kadie:
Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning.
153-157
- Adam Kowalczyk, Herman L. Ferrá, Ken Gardiner:
Discovering Production Rules with Higher Order Neural Networks.
158-162
- Bing Leng, Bruce G. Buchanan:
Constructive Induction on Symbolic Features.
163-167
- Xiaofeng Ling, Malur Aji Narayan:
Comparison of Methods Based on Inverse Resolution.
168-172
- Christopher J. Matheus:
The Need for Constructive Induction.
173-177
- Raymond J. Mooney, Dirk Ourston:
Constructive Induction in Theory Refinement.
178-182
- Patrick M. Murphy, Michael J. Pazzani:
Constructive Induction of M-of-N Terms.
183-187
- Harish Ragavan, Larry A. Rendell:
Relations, Knowledge and Empirical Learning.
188-192
- Arlindo L. Oliveira, Alberto L. Sangiovanni-Vincentelli:
Learning Concepts by Synthesizing Minimal Threshold Gate Networks.
193-197
- Sharad Saxena:
On the Effect of Instance Representation on Generalization.
198-202
- Glenn Silverstein, Michael J. Pazzani:
Relational Clichés: Constraining Induction During Relational Learning.
203-207
- Richard S. Sutton, Christopher J. Matheus:
Learning Polynomial Functions by Feature Construction.
208-212
- Geoffrey G. Towell, Mark Craven, Jude W. Shavlik:
Constructive Induction in Knowledge-Based Neural Networks.
213-217
- Larry Watanabe, Larry A. Rendell:
Feature Construction in Structural Decision Trees.
218-222
- Der-Shung Yang, Larry A. Rendell, Gunnar Blix:
Fringe-Like Feature Construction: A Comparative Study and a Unifying Scheme.
223-227
- Dit-Yan Yeung:
A Neural Network Approach to Constructive Induction.
228-232
Learning in Intelligent Information Retrieval
Learning Reaction Strategies
- Matthew Brand:
Decision-Theoretic Learning in an Action System.
283-287
- Steve A. Chien, Melinda T. Gervasio, Gerald DeJong:
On Becoming Decreasingly Reactive: Learning to Deliberate Minimally.
288-292
- Helen G. Cobb, John J. Grefenstette:
Learning the Persistence of Actions in Reactive Control Rules.
292-297
- José del R. Millán, Carme Torras:
Learning to Avoid Obstacles Through Reinforcement.
298-302
- Goang-Tay Hsu, Reid G. Simmons:
Learning Football Evaluation for a Walking Robot.
303-307
- Smadar Kedar, John L. Bresina, C. Lisa Dent:
The Blind Leading the Blind: Mutual Refinement of Approximate Theories.
308-312
- Mieczyslaw M. Kokar, Spyros A. Reveliotis:
Learning to Select a Model in a Changing World.
313-317
- Bruce Krulwich:
Learning from Deliberated Reactivity.
318-322
- Long Ji Lin:
Self-improvement Based on Reinforcement Learning, Planning and Teaching.
323-327
- Sridhar Mahadevan, Jonathan Connell:
Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption Architecture.
328-332
- Andrew W. Moore:
Variable Resolution Dynamic Programming.
333-337
- David R. Pierce:
Learning a Set of Primitive Actions with an Uninterpreted Sensorimotor Apparatus.
338-342
- Mark B. Ring:
Incremental Development of Complex Behaviors.
343-347
- Satinder P. Singh:
Transfer of Learning Across Compositions of Sequentail Tasks.
348-352
- Richard S. Sutton:
Planning by Incremental Dynamic Programming.
353-357
- Ming Tan:
Learning a Cost-Sensitive Internal Representation for Reinforcement Learning.
358-362
- Steven D. Whitehead:
Complexity and Cooperation in Q-Learning.
363-367
- Lambert E. Wixson:
Scaling Reinforcement Learning Techniques via Modularity.
3368-372
Learning Relations
- John A. Allen, Kevin Thompson:
Probabilistic Concept Formation in Relational Domains.
375-379
- Michael Bain:
Experiments in Non-Monotonic Learning.
380-384
- Ivan Bratko, Stephen Muggleton, Alen Varsek:
Learning Qualitative Models of Dynamic Systems.
385-388
- Clifford Brunk, Michael J. Pazzani:
An Investigation of Noise-Tolerant Relational Concept Learning Algorithms.
389-393
- Luc De Raedt, Maurice Bruynooghe, Bern Martens:
Integrity Constraints and Interactive Concept-Learning.
394-398
- Saso Dzeroski, Nada Lavrac:
Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL.
399-402
- C. Feng:
Inducing Temporal Fault Diagnostic Rules from a Qualitative Model.
403-406
- Kazuo Hiraki, John H. Gennari, Yoshinobu Yamamoto, Yuichiro Anzai:
Learning Spatial Relations from Images.
407-411
- David Humme, Claude Sammut:
Using Inverse Resolution to Learn Relations from Experiments.
412-416
- Boonserm Kijsirikul, Masayuki Numao, Masamichi Shimura:
Efficient Learning of Logic Programs with Non-determinant, Non-discriminating Literals.
417-421
- Christopher Leckie, Ingrid Zukerman:
Learning Search Control Rules for Planning: An Inductive Approach.
422-426
- C. David Page Jr., Alan M. Frisch:
Learning Constrained Atoms.
427-431
- Michael J. Pazzani, Clifford Brunk, Glenn Silverstein:
A Knowledge-intensive Approach to Learning Relational Concepts.
432-436
- Zhaogang Qian, Keki B. Irani:
The Consistent Concept Axiom.
437-441
- J. Ross Quinlan:
Determinate Literals in Inductive Logic Programming.
442-446
- Bradley L. Richards, Raymond J. Mooney:
First-Order Theory Revision.
447-451
- Céline Rouveirol:
Completeness for Inductive Procedures.
452-456
- Rüdiger Wirth, Paul O'Rorke:
Constraints on Predicate Invention.
457-461
- James Wogulis:
Revising Relational Domain Theories.
462-466
- Kenji Yamanishi, Akihiko Konagaya:
Learning Stochastic Motifs from Genetic Sequences.
467-471
Learning From Theory and Data
- Hamid R. Berenji:
Refinement of Approximate Reasoning-based Controllers by Reinforcement Learning.
475-479
- Marco Botta, S. Ravotto, Lorenza Saitta, S. B. Sperotto:
Improving Learning Using Causality and Abduction.
480-484
- Timothy Cain:
The DUCTOR: A Theory Revision System for Propositional Domains.
485-489
- William W. Cohen:
The Generality of Overgenerality.
490-494
- Marie desJardins:
Probabilistic Evaluating of Bias for Learning Systems.
495-499
- Ronen Feldman, Alberto Maria Segre, Moshe Koppel:
Incremental Refinement of Approximate Domain Theories.
500-504
- Diana F. Gordon:
An Enhancer for Reactive Plans.
505-508
- Jonathan Gratch, Gerald DeJong:
A Hybrid Approach to Guaranteed Effective Control Strategies.
509-513
- Rei Hamakawa:
Revision Cost for Theory Refinement.
514-518
- Xiaofeng Ling, Marco Valtorta:
Revision of Reduced Theories.
519-523
- Richard Maclin, Jude W. Shavlik:
Refining Domain Theories Expressed as Finite-State Automata.
524-528
- Claire Nedellec:
A Smallest Generalization Step Strategy.
529-533
- Dirk Ourston, Raymond J. Mooney:
Improving Shared Rules in Multiple Category Domain Theories.
534-538
- Wei-Min Shen:
Discovering Regularities from Large Knowledge Bases.
539-543
- Prasad Tadepalli:
Learning with Incrutable Theories.
544-548
- Gheorghe Tecuci, Ryszard S. Michalski:
A Method for Multistrategy Task-Adaptive Learning Based on Plausible Justifications.
549-553
- Kevin Thompson, Pat Langley, Wayne Iba:
Using Background Knowledge in Concept Formation.
554-558
- Bradley L. Whitehall, Stephen C. Y. Lu:
A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems.
559-563
- Edward J. Wisniewski, Douglas L. Medin:
Is it a Pocket or a Purse? Tighly Coupled Theory and Data Driven Learing.
564-568
- Jungsoon P. Yoo, Douglas H. Fisher:
Identifying Cost Effective Boundaries of Operationality.
569-573
Machine Learning in Engineering Automation
- Steve A. Chien, Bradley L. Whitehall, Thomas G. Dietterich, Richard J. Doyle, Brian Falkenhainer, James Garrett, Stephen C. Y. Lu:
Machine Learning in Engineering Automation.
577-580
- Leonid V. Belyaev, Loretta P. Falcone:
Noise-Resistant Classification.
581-585
- Scott Bennett, Gerald DeJong:
Comparing Stochastic Planning to the Acquisition of Increasingly Permissive Plans.
586-590
- Gautam Biswas, Jerry B. Weinberg, Qian Yang, Glenn R. Koller:
Conceptual Clustering and Exploratory Data Analysis.
591-595
- Jason Catlett:
Megainduction: A Test Flight.
596-599
- Giuseppe Cerbone, Thomas G. Dietterich:
Knowledge Compilation to Speed Up Numerical Optimization.
600-604
- Ashok K. Goel:
Model Revision: A Theory of Incremental Model Learning.
605-609
- Jürgen Herrmann:
Learning Analytical Knowledge About VLSI-Design from Observation.
610-614
- Carl Myers Kadie:
Continous Conceptual Set Covering: Learning Robot Operators From Examples.
615-619
- Paul O'Rorke, Steven Morris, Michael Amirfathi, William Bond, Daniel C. St. Clair:
Machine Learning for Nondestructive Evaluation.
620-624
- Peter Pachowicz, Jerzy W. Bala:
Improving Recognition Effectiveness of Noisy Texture Concepts.
625-629
- R. Bharat Rao, Stephen C. Y. Lu, Robert E. Stepp:
Knowledge-Based Equation Discovery in Engineering Domains.
630-634
- Yoram Reich:
Design Integrated Learning Systems for Engineering Design.
635-639
- Jeffrey C. Schlimmer:
Database Consistency via Inductive Learning.
640-644
- David K. Tcheng, Bruce L. Lambert, Stephen C. Y. Lu, Larry A. Rendell:
AIMS: An Adaptive Interactive Modeling System for Supporting Engineering Decision Making.
645-649
- Larry Watanabe, Sudhakar Yerramareddy:
Decision Tree Induction of 3-D Manufacturing Features.
650-654
Addendum
Copyright © Mon Nov 2 20:46:37 2009
by Michael Ley (ley@uni-trier.de)
