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ORCID8th ML 1991
- Lawrence Birnbaum, Gregg Collins:
Proceedings of the Eighth International Workshop (ML91), Northwestern University, Evanston, Illinois, USA. Morgan Kaufmann 1991, ISBN 1-55860-200-3
Automated Knowledge Acquisition
- Thomas R. Gruber, Catherine Baudin, John H. Boose, Jay Webber:
Design Rationale Capture as Knowledge Acquisition. 3-12 - Yolanda Gil:
A Domain-Independent Framework for Effective Experimentation in Planning. 13-17 - Eric K. Jones:
Knowledge Refinement Using a High Level, Non-Technical Vocabulary. 18-22 - Yong Ma, David C. Wilkins:
Improving the Performance of Inconsistent Knowledge Bases via Combined Optimization Method. 23-27 - Susan Craw, Derek H. Sleeman:
The Flexibility of Speculative Refinement. 28-32 - Michael A. Weintraub, Tom Bylander:
Generating Error Candidates for Assigning Blame in a Knowledge Base. 33-37
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. ML 1991: 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 W. 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
- David D. Lewis:
Learning in Intelligent Information Retrieval. 235-239 - Jay N. Bhuyan, Vijay V. Raghavan:
A Probabilistic Retrieval Scheme for Cluster-based Adaptive Information Retrieval. 240-244 - Stuart L. Crawford, Robert M. Fung, Lee A. Appelbaum, Richard M. Tong:
Classification Trees for Information Retrieval. 245-249 - Sanjiv K. Bhatia, Jitender S. Deogun, Vijay V. Raghavan:
Query Formulation Through Knowledge Acquisition. 250-254 - A. Goker, Thomas Leo McCluskey:
Incremental Learning in a Probalistic Information Retrieval System. 255-259 - K. L. Kwok:
Query Learning Using an ANN with Adaptive Architecture. 260-264 - Ashwin Ram, Lawrence Hunter:
A Goal-Based Approach to Intelligent Information Retrieval. 265-269 - Paul Thompson:
Machine Learning in the Combination of Expert Opinion Approach to IR. 270-274 - Steven Walczak:
Predicting Actions from Induction on Past Performance. 275-279
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. 368-372
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 H. 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 M. 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. ML 1991: 581-585 - Scott W. 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 E. 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
- Mario Martín, Ramon Sangüesa, Ulises Cortés:
Knowledge Acquisition Combining Analytical and Empirrcal Techniques. 657-661
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