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ORCID6th ML 1989: Cornell University, Ithaca, New York, USA
- Alberto Maria Segre:
Proceedings of the Sixth International Workshop on Machine Learning (ML 1989), Cornell University, Ithaca, New York, USA, June 26-27, 1989. Morgan Kaufmann 1989, ISBN 1-55860-036-1
Combining Empirical and Explanation-Based Learning
- Pat Langley:
Unifying Themes in Empirical and Explanation-Based Learning. ML 1989: 2-4 - Raymond J. Mooney, Dirk Ourston:
Induction Over the Unexplained: Integrated Learning of Concepts with Both Explainable and Conventional Aspects. ML 1989: 5-7 - Jungsoon P. Yoo, Douglas H. Fisher:
Conceptual Clustering of Explanations. ML 1989: 8-10 - Gerhard Widmer:
A Tight Integration of Deductive Learning. ML 1989: 11-13 - Gheorghe Tecuci, Yves Kodratoff:
Multi-Strategy Learning in Nonhomongeneous Domain Theories. ML 1989: 14-16 - Jianping Zhang, Ryszard S. Michalski:
A Description of Preference Criterion in Constructive Learning: A Discussion of Basis Issues. ML 1989: 17-19 - Michael Redmond:
Combining Case-Based Reasoning, Explanation-Based Learning, and Learning form Instruction. ML 1989: 20-22 - Francesco Bergadano, Attilio Giordana, S. Ponsero:
Deduction in Top-Down Inductive Learning. ML 1989: 23-25 - Wendy Sarrett, Michael J. Pazzani:
One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning. ML 1989: 26-28 - Haym Hirsh:
Combining Empirical and Analytical Learning with Version Spaces. ML 1989: 29-33 - Andrea Pohoreckyj Danyluk:
Finding New Rules for Incomplete Theories: Explicit Biases for Induction with Contextual Information. ML 1989: 34-36 - Tom Fawcett:
Learning from Plausible Explanations. ML 1989: 37-39 - Kamal M. Ali:
Augmenting Domain Theory for Explanation-Based Generalization. ML 1989: 40-42 - David Haines:
Explanation Based Learning as Constrained Search. ML 1989: 43-45 - Steven Morris:
Reducing Search and Learning Goal Preferences. ML 1989: 46-48 - Alex Kass:
Adaptation-Based Explanation: Explanations as Cases. ML 1989: 49-51 - Colleen M. Seifert:
A Retrieval Model Using Feature Selection. ML 1989: 52-54 - Bruce Krulwich, Gregg Collins, Lawrence Birnbaum:
Improving Decision-Making on the Basis of Experience. ML 1989: 55-57 - Masayuki Numao, Masamichi Shimura:
Explanation-Based Acceleration of Similarity-Based Learning. ML 1989: 58-60 - Lawrence Hunter:
Knowledge Acquisition Planning: Results and Prospects. ML 1989: 61-65 - Joachim Diederich:
"Learning by Instruction" in connectionist Systems. ML 1989: 66-68 - Bruce F. Katz:
Integrating Learning in a Neural Network. ML 1989: 69-71 - Michael J. Pazzani:
Explanation-Based Learning with Week Domain Theories. ML 1989: 72-74 - Gerhard Friedrich, Wolfgang Nejdl:
Using Domain Knowledge to Improve Inductive Learning Algorithms for Diagnosis. ML 1989: 75-77 - James Wogulis:
A Framework for Improving Efficiency and Accuracy. ML 1989: 78-80 - George Drastal, Regine Meunier, Stan Raatz:
Error Correction in Constructive Induction. ML 1989: 81-83 - Ralph Barletta, Randy Kerber:
Improving Explanation-Based Indexing with Empirical Learning. ML 1989: 84-86 - Michael Wollowski:
A Schema for an Integrated Learning System. ML 1989: 87-89 - Jude W. Shavlik, Geoffrey G. Towell:
Combining Explanation-Based Learning and Artificial Neural Networks. ML 1989: 90-93
Empirical Learning: Theory and Application
- Wray L. Buntine:
Learning Classification Rules Using Bayes. ML 1989: 94-98 - Matjaz Gams, Aram Karalic:
New Empirical Learning Mechanisms Perform Significantly Better in Real Life Domains. ML 1989: 99-103 - Philip K. Chan:
Inductive Learning with BCT. ML 1989: 104-108 - Ritchey A. Ruff, Thomas G. Dietterich:
What Good Are Experiments?. ML 1989: 109-112 - Stephen H. Muggleton, Michael Bain, Jean Hayes Michie, Donald Michie:
An Experimental Comparison of Human and Machine Learning Formalisms. ML 1989: 113-118 - Giulia Pagallo, David Haussler:
Two Algorithms That Learn DNF by Discovering Relevant Features. ML 1989: 119-123 - Thomas G. Dietterich:
Limitations on Inductive Learning. ML 1989: 124-128 - Rodney M. Goodman, Padhraic Smyth:
The Induction of Probabilistic Rule Sets - The Itrule Algorithm. ML 1989: 129-132 - Lawrence B. Holder:
Empirical Substructure Discovery. ML 1989: 133-136 - Jan Paredis:
Learning the Behavior of Dynamical Systems form Examples. ML 1989: 137-140 - Matthew T. Mason, Alan D. Christiansen, Tom M. Mitchell:
Experiments in Robot Learning. ML 1989: 141-145 - W. Scott Spangler, Usama M. Fayyad, Ramasamy Uthurusamy:
Induction of Decision Trees from Inconclusive Data. ML 1989: 146-150 - Michel Manago:
Knowledge Intensive Induction. ML 1989: 151-155 - Brian R. Gaines:
An Ounce of Knowledge is Worth a Ton of Data: Quantitative studies of the Trade-Off between Expertise and Data Based On Statistically Well-Founded Empirical Induction. ML 1989: 156-159 - Kent A. Spackman:
Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning. ML 1989: 160-163 - J. Ross Quinlan:
Unknown Attribute Values in Induction. ML 1989: 164-168 - Douglas H. Fisher, Kathleen B. McKusick, Raymond J. Mooney, Jude W. Shavlik, Geoffrey G. Towell:
Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems. ML 1989: 169-173 - Cullen Schaffer:
Bacon, Data Analysis and Artificial Intelligence. ML 1989: 174-179
Learning Plan Knowledge
- David Rudy, Dennis F. Kibler:
Learning to Plan in Complex Domains. ML 1989: 180-182 - Jude W. Shavlik:
An Empirical Analysis of EBL Approaches for Learning Plan Schemata. ML 1989: 183-187 - Mike R. Hilliard, Gunar E. Liepins, Gita Rangarajan, Mark R. Palmer:
Learning Decision Rules for scheduling Problems: A Classifier Hybrid Approach. ML 1989: 188-190 - Keith R. Levi, David L. Perschbacher, Valerie L. Shalin:
Learning Tactical Plans for Pilot Aiding. ML 1989: 191-193 - Lawrence Birnbaum, Gregg Collins, Bruce Krulwich:
Issues in the Justification-Based Diagnosis of Planning Failures. ML 1989: 194-196 - Stan Matwin, Johanne Morin:
Learning Procedural Knowledge in the EBG Context. ML 1989: 197-199 - Jean-Francois Puget:
Learning Invariants from Explanations. ML 1989: 200-204 - Ralph P. Sobek, Jean-Paul Laumond:
Using Learning to Recover Side-Effects of Operators in Robotics. ML 1989: 205-208 - Paul O'Rorke, Timothy Cain, Andrew Ortony:
Learning to Recognize Plans Involving Affect. ML 1989: 209-211 - Randolph M. Jones:
Learning to Retrieve Useful Information for Problem Solving. ML 1989: 212-214 - Kurt VanLehn:
Discovering Problem Solving Strategies: What Humans Do and Machines Don't (Yet). ML 1989: 215-217 - Melissa P. Chase, Monte Zweben, Richard L. Piazza, John D. Burger, Paul P. Maglio, Haym Hirsh:
Approximating Learned Search Control Knowledge. ML 1989: 218-220 - Prasad Tadepalli:
Planning Approximate Plans for Use in the Real World. ML 1989: 224-228 - John A. Allen, Pat Langley:
Using Concept Hierarchies to Organize Plan Knowledge. ML 1989: 229-231 - Hua Yang, Douglas H. Fisher:
Conceptual Clustering of Mean-Ends Plans. ML 1989: 232-234 - Nicholas S. Flann:
Learning Appropriate Abstractions for Planning in Formation Problems. ML 1989: 235-239 - Jack Mostow, Armand Prieditis:
Discovering Admissible Search Heuristics by Abstracting and Optimizing. ML 1989: 240-240 - Craig A. Knoblock:
Learning Hierarchies of Abstraction Spaces. ML 1989: 241-245 - Timothy M. Converse, Kristian J. Hammond, Mitchell Marks:
Learning from Opportunity. ML 1989: 246-248 - Steve A. Chien:
Learning by Analyzing Fortuitous Occurrences. ML 1989: 249-251 - Melinda T. Gervasio, Gerald DeJong:
Explanation-Based Learning of Reactive Operations. ML 1989: 252-254 - Jim Blythe, Tom M. Mitchell:
On Becoming Reactive. ML 1989: 255-259
Knowledge-Based Refinement and Theory Revision
- Allen Ginsberg:
Knowledge Base Refinement and Theory Revision. ML 1989: 260-265 - Paul O'Rorke, Steven Morris, David Schulenburg:
Theory Formation by Abduction: Initial Results of a Case Study Based on the Chemical Revolution. ML 1989: 266-271 - Donald Rose:
Using Domain Knowledge to Aid Scientific Theory Revision. ML 1989: 272-277 - Deepak Kulkarni, Herbert A. Simon:
The Role of Experimentation in Scientific Theory Revision. ML 1989: 278-283 - Shankar A. Rajamoney:
Exemplar-Based Theory Rejection: An Approach to the Experience Consistency Problem. ML 1989: 284-289 - Kenneth S. Murray, Bruce W. Porter:
Controlling Search for the Consequences of New Information During Knowledge Integration. ML 1989: 290-295 - Keith R. Levi, Valerie L. Shalin, David L. Perschbacher:
Identifying Knowledge Base Deficiencies by Observing User Behavior. ML 1989: 296-301 - Chris Tong, Phil Franklin:
Toward Automated Rational Reconstruction: A Case Study. ML 1989: 302-307 - Michael H. Sims, John L. Bresina:
Discovering Mathematical Operation Definitions. ML 1989: 308-313 - Zbigniew W. Ras, Maria Zemankova:
Imprecise Concept Learning within a Growing Language. ML 1989: 314-319 - Sridhar Mahadevan:
Using Determinations in EBL: A Solution to the incomplete Theory Problem. ML 1989: 320-325 - Marco Valtorta:
Some Results on the Complexity of Knowledge-Based Refinement. ML 1989: 326-331 - David C. Wilkins, Kok-Wah Tan:
Knowledge Base Refinement as Improving an Incorrect, Inconsistent and Incomplete Domain Theory. ML 1989: 332-339
Incremental Learning
- John J. Grefenstette:
Incremental Learning of Control Strategies with Genetic algorithms. ML 1989: 340-344 - Charles W. Anderson:
Tower of Hanoi with Connectionist Networks: Learning New Features. ML 1989: 345-349 - Leslie Pack Kaelbling:
A Formal Framework for Learning in Embedded Systems. ML 1989: 350-353 - Steven D. Whitehead, Dana H. Ballard:
A Role for Anticipation in Reactive Systems that Learn. ML 1989: 354-357 - Paul D. Scott, Shaul Markovitch:
Uncertainty Based Selection of Learning Experiences. ML 1989: 358-361 - Paul E. Utgoff:
Improved Training Via Incremental Learning. ML 1989: 362-365 - Scott H. Clearwater, Tze-Pin Chen, Haym Hirsh, Bruce G. Buchanan:
Incremental Batch Learning. ML 1989: 366-370 - Kevin Thompson, Pat Langley:
Incremental Concept Formation with Composite Objects. ML 1989: 371-374 - Rich Caruana, J. David Schaffer, Larry J. Eshelman:
Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms. ML 1989: 375-378 - John H. Gennari:
Focused Concept Formation. ML 1989: 379-382 - Antoine Cornuéjols:
An Exploration Into Incremental Learning: the INFLUENCE System. ML 1989: 383-386 - David W. Aha:
Incremental, Instance-Based Learning of Independent and Graded Concept Descriptions. ML 1989: 387-391 - Ming Tan, Jeffrey C. Schlimmer:
Cost-Sensitive Concept Learning of Sensor Use in Approach ad Recognition. ML 1989: 392-395 - Joel D. Martin:
Reducing Redundant Learning. ML 1989: 396-399 - Jakub Segen:
Incremental Clustering by Minimizing Representation Length. ML 1989: 400-403 - Shaul Markovitch, Paul D. Scott:
Information Filters and Their Implementation in the SYLLOG System. ML 1989: 404-407 - Eric Wefald, Stuart J. Russell:
Adaptive Learning of Decision-Theoretic Search Control Knowledge. ML 1989: 408-411 - Oliver G. Selfridge:
Atoms of Learning II: Adaptive Strategies A Study of Two-Person Zero-Sum Competition. ML 1989: 412-415 - Terence C. Fogarty:
An Incremental Genetic Algorithm for Real-Time Learning. ML 1989: 416-419 - Ronald R. Yager, Kenneth M. Ford:
Participatory Learning: A Constructivist Model. ML 1989: 420-425
Representational Issues in Machine Learning
- Devika Subramanian:
Representational Issues in Machine Learning. ML 1989: 426-429 - John Woodfill:
Labor Saving New Distinctions. ML 1989: 430-433 - Devika Subramanian:
A Theory of Justified Reformulations. ML 1989: 434-438 - Patricia J. Riddle:
Reformation from State Space to Reduction Space. ML 1989: 439-440 - James P. Callan:
Knowledge-Based Feature Generation. ML 1989: 441-443 - Richard Maclin, Jude W. Shavlik:
Enriching Vocabularies by Generalizing Explanation Structures. ML 1989: 444-446 - Scott Dietzen, Frank Pfenning:
Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization. ML 1989: 447-449 - Russell Greiner:
Towards a Formal Analysis of EBL. ML 1989: 450-453 - Robert C. Holte, Robert M. Zimmer:
A Mathematical Framework for Studying Representation. ML 1989: 454-456 - Jeffrey C. Schlimmer:
Refining Representations to Improve Problem Solving Quality. ML 1989: 457-460 - Larry A. Rendell:
Comparing Systems and analyzing Functions to Improve Constructive Induction. ML 1989: 461-464 - Sharad Saxena:
Evaluating alternative Instance Representations. ML 1989: 465-468 - Lonnie Chrisman:
Evaluating Bias During Pac-Learning. ML 1989: 469-471 - Pankaj Mehra:
Constructive Induction Framework. ML 1989: 474-475 - Luc De Raedt, Maurice Bruynooghe:
Constructive Induction by Analogy. ML 1989: 476-477 - Mieczyslaw M. Kokar:
Concept Discovery Through Utilization of Invariance Embedded in the Description Language. ML 1989: 478-479 - Benjamin N. Grosof, Stuart J. Russell:
Declarative Bias for Structural Domains. ML 1989: 480-482 - Sunil Mohan, Chris Tong:
Automatic Construction of a Hierarchical Generate-and-Test Algorithm. ML 1989: 483-484 - Jane Yung-jen Hsu:
A Knowledge-Level Analysis of Informing. ML 1989: 485-488 - Jack Mostow:
An Object-Oriented Representation for Search algorithms. ML 1989: 489-491 - Richard M. Keller:
Compiling Learning Vocabulary from a Performance System Description. ML 1989: 482-495 - Bruce L. Lambert, David K. Tcheng, Stephen C. Y. Lu:
Generalized Recursive Splitting Algorithms for Learning Hybrid Concepts. ML 1989: 496-498 - Diana F. Gordon:
Screening Hypotheses with Explicit Bias. ML 1989: 499-500 - Christian de Sainte Marie:
Building A Learning Bias from Perceived Dependencies. ML 1989: 501-502 - Katharina Morik, Jörg-Uwe Kietz:
A Bootstrapping Approach to Concept Clustering. ML 1989: 503-504 - Hans Tallis:
Overcoming Feature Space Bias in a Reactive Environment. ML 1989: 505-508
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