6. ML 1989:
Cornell University,
Ithaca,
New York,
USA
Alberto Maria Segre (Ed.):
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.
2-4
- Raymond J. Mooney, Dirk Ourston:
Induction Over the Unexplained: Integrated Learning of Concepts with Both Explainable and Conventional Aspects.
5-7
- Jungsoon P. Yoo, Douglas H. Fisher:
Conceptual Clustering of Explanations.
8-10
- Gerhard Widmer:
A Tight Integration of Deductive Learning.
11-13
- Gheorghe Tecuci, Yves Kodratoff:
Multi-Strategy Learning in Nonhomongeneous Domain Theories.
14-16
- Jianping Zhang, Ryszard S. Michalski:
A Description of Preference Criterion in Constructive Learning: A Discussion of Basis Issues.
17-19
- Michael Redmond:
Combining Case-Based Reasoning, Explanation-Based Learning, and Learning form Instruction.
20-22
- Francesco Bergadano, Attilio Giordana, S. Ponsero:
Deduction in Top-Down Inductive Learning.
23-25
- Wendy Sarrett, Michael J. Pazzani:
One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning.
26-28
- Haym Hirsh:
Combining Empirical and Analytical Learning with Version Spaces.
29-33
- Andrea Pohoreckyj Danyluk:
Finding New Rules for Incomplete Theories: Explicit Biases for Induction with Contextual Information.
34-36
- Tom Fawcett:
Learning from Plausible Explanations.
37-39
- Kamal M. Ali:
Augmenting Domain Theory for Explanation-Based Generalization.
40-42
- David Haines:
Explanation Based Learning as Constrained Search.
43-45
- Steven Morris:
Reducing Search and Learning Goal Preferences.
46-48
- Alex Kass:
Adaptation-Based Explanation: Explanations as Cases.
49-51
- Colleen M. Seifert:
A Retrieval Model Using Feature Selection.
52-54
- Bruce Krulwich, Gregg Collins, Lawrence Birnbaum:
Improving Decision-Making on the Basis of Experience.
55-57
- Masayuki Numao, Masamichi Shimura:
Explanation-Based Acceleration of Similarity-Based Learning.
58-60
- Lawrence Hunter:
Knowledge Acquisition Planning: Results and Prospects.
61-65
- Joachim Diederich:
"Learning by Instruction" in connectionist Systems.
66-68
- Bruce F. Katz:
Integrating Learning in a Neural Network.
69-71
- Michael J. Pazzani:
Explanation-Based Learning with Week Domain Theories.
72-74
- Gerhard Friedrich, Wolfgang Nejdl:
Using Domain Knowledge to Improve Inductive Learning Algorithms for Diagnosis.
75-77
- James Wogulis:
A Framework for Improving Efficiency and Accuracy.
78-80
- George Drastal, Regine Meunier, Stan Raatz:
Error Correction in Constructive Induction.
81-83
- Ralph Barletta, Randy Kerber:
Improving Explanation-Based Indexing with Empirical Learning.
84-86
- Michael Wollowski:
A Schema for an Integrated Learning System.
87-89
- Jude W. Shavlik, Geoffrey G. Towell:
Combining Explanation-Based Learning and Artificial Neural Networks.
90-93
Empirical Learning:
Theory and Application
- Wray L. Buntine:
Learning Classification Rules Using Bayes.
94-98
- Matjaz Gams, Aram Karalic:
New Empirical Learning Mechanisms Perform Significantly Better in Real Life Domains.
99-103
- Philip K. Chan:
Inductive Learning with BCT.
104-108
- Ritchey A. Ruff, Thomas G. Dietterich:
What Good Are Experiments?.
109-112
- Stephen Muggleton, Michael Bain, Jean Hayes Michie, Donald Michie:
An Experimental Comparison of Human and Machine Learning Formalisms.
113-118
- Giulia Pagallo, David Haussler:
Two Algorithms That Learn DNF by Discovering Relevant Features.
119-123
- Thomas G. Dietterich:
Limitations on Inductive Learning.
124-128
- Rodney M. Goodman, Padhraic Smyth:
The Induction of Probabilistic Rule Sets - The Itrule Algorithm.
129-132
- Lawrence B. Holder:
Empirical Substructure Discovery.
133-136
- Jan Paredis:
Learning the Behavior of Dynamical Systems form Examples.
137-140
- Matthew T. Mason, Alan D. Christiansen, Tom M. Mitchell:
Experiments in Robot Learning.
141-145
- W. Scott Spangler, Usama M. Fayyad, Ramasamy Uthurusamy:
Induction of Decision Trees from Inconclusive Data.
146-150
- Michel Manago:
Knowledge Intensive Induction.
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.
156-159
- Kent A. Spackman:
Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning.
160-163
- J. Ross Quinlan:
Unknown Attribute Values in Induction.
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.
169-173
- Cullen Schaffer:
Bacon, Data Analysis and Artificial Intelligence.
174-179
Learning Plan Knowledge
- David Rudy, Dennis F. Kibler:
Learning to Plan in Complex Domains.
180-182
- Jude W. Shavlik:
An Empirical Analysis of EBL Approaches for Learning Plan Schemata.
183-187
- Mike R. Hilliard, Gunar E. Liepins, G. Rangarajan, Mark Palmer:
Learning Decision Rules for scheduling Problems: A Classifier Hybrid Approach.
188-190
- Keith R. Levi, David L. Perschbacher, Valerie L. Shalin:
Learning Tactical Plans for Pilot Aiding.
191-193
- Lawrence Birnbaum, Gregg Collins, Bruce Krulwich:
Issues in the Justification-Based Diagnosis of Planning Failures.
194-196
- Stan Matwin, Johanne Morin:
Learning Procedural Knowledge in the EBG Context.
197-199
- Jean-Francois Puget:
Learning Invariants from Explanations.
200-204
- Ralph P. Sobek, Jean-Paul Laumond:
Using Learning to Recover Side-Effects of Operators in Robotics.
205-208
- Paul O'Rorke, Timothy Cain, Andrew Ortony:
Learning to Recognize Plans Involving Affect.
209-211
- Randolph M. Jones:
Learning to Retrieve Useful Information for Problem Solving.
212-214
- Kurt VanLehn:
Discovering Problem Solving Strategies: What Humans Do and Machines Don't (Yet).
215-217
- Melissa P. Chase, Monte Zweben, Richard L. Piazza, John D. Burger, Paul P. Maglio, Haym Hirsh:
Approximating Learned Search Control Knowledge.
218-220
- Prasad Tadepalli:
Planning Approximate Plans for Use in the Real World.
224-228
- John A. Allen, Pat Langley:
Using Concept Hierarchies to Organize Plan Knowledge.
229-231
- Hua Yang, Douglas H. Fisher:
Conceptual Clustering of Mean-Ends Plans.
232-234
- Nicholas S. Flann:
Learning Appropriate Abstractions for Planning in Formation Problems.
235-239
- Jack Mostow, Armand Prieditis:
Discovering Admissible Search Heuristics by Abstracting and Optimizing.
240-240
- Craig A. Knoblock:
Learning Hierarchies of Abstraction Spaces.
241-245
- Timothy M. Converse, Kristian J. Hammond, Mitchell Marks:
Learning from Opportunity.
246-248
- Steve A. Chien:
Learning by Analyzing Fortuitous Occurrences.
249-251
- Melinda T. Gervasio, Gerald DeJong:
Explanation-Based Learning of Reactive Operations.
252-254
- Jim Blythe, Tom M. Mitchell:
On Becoming Reactive.
255-259
Knowledge-Based Refinement and Theory Revision
- Allen Ginsberg:
Knowledge Base Refinement and Theory Revision.
260-265
- Paul O'Rorke, Steven Morris, David Schulenburg:
Theory Formation by Abduction: Initial Results of a Case Study Based on the Chemical Revolution.
266-271
- Donald Rose:
Using Domain Knowledge to Aid Scientific Theory Revision.
272-277
- Deepak Kulkarni, Herbert A. Simon:
The Role of Experimentation in Scientific Theory Revision.
278-283
- Shankar A. Rajamoney:
Exemplar-Based Theory Rejection: An Approach to the Experience Consistency Problem.
284-289
- Kenneth S. Murray, Bruce W. Porter:
Controlling Search for the Consequences of New Information During Knowledge Integration.
290-295
- Keith R. Levi, Valerie L. Shalin, David L. Perschbacher:
Identifying Knowledge Base Deficiencies by Observing User Behavior.
296-301
- Chris Tong, Phil Franklin:
Toward Automated Rational Reconstruction: A Case Study.
302-307
- Michael H. Sims, John L. Bresina:
Discovering Mathematical Operation Definitions.
308-313
- Zbigniew W. Ras, Maria Zemankova:
Imprecise Concept Learning within a Growing Language.
314-319
- Sridhar Mahadevan:
Using Determinations in EBL: A Solution to the incomplete Theory Problem.
320-325
- Marco Valtorta:
Some Results on the Complexity of Knowledge-Based Refinement.
326-331
- David C. Wilkins, Kok-Wah Tan:
Knowledge Base Refinement as Improving an Incorrect, Inconsistent and Incomplete Domain Theory.
332-339
Incremental Learning
- John J. Grefenstette:
Incremental Learning of Control Strategies with Genetic algorithms.
340-344
- Charles W. Anderson:
Tower of Hanoi with Connectionist Networks: Learning New Features.
345-349
- Leslie Pack Kaelbling:
A Formal Framework for Learning in Embedded Systems.
350-353
- Steven D. Whitehead, Dana H. Ballard:
A Role for Anticipation in Reactive Systems that Learn.
354-357
- Paul D. Scott, Shaul Markovitch:
Uncertainty Based Selection of Learning Experiences.
358-361
- Paul E. Utgoff:
Improved Training Via Incremental Learning.
362-365
- Scott H. Clearwater, Tze-Pin Chen, Haym Hirsh, Bruce G. Buchanan:
Incremental Batch Learning.
366-370
- Kevin Thompson, Pat Langley:
Incremental Concept Formation with Composite Objects.
371-374
- Rich Caruana, J. David Schaffer, Larry J. Eshelman:
Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms.
375-378
- John H. Gennari:
Focused Concept Formation.
379-382
- Antoine Cornuéjols:
An Exploration Into Incremental Learning: the INFLUENCE System.
383-386
- David W. Aha:
Incremental, Instance-Based Learning of Independent and Graded Concept Descriptions.
387-391
- Ming Tan, Jeffrey C. Schlimmer:
Cost-Sensitive Concept Learning of Sensor Use in Approach ad Recognition.
392-395
- Joel D. Martin:
Reducing Redundant Learning.
396-399
- Jakub Segen:
Incremental Clustering by Minimizing Representation Length.
400-403
- Shaul Markovitch, Paul D. Scott:
Information Filters and Their Implementation in the SYLLOG System.
404-407
- Eric Wefald, Stuart J. Russell:
Adaptive Learning of Decision-Theoretic Search Control Knowledge.
408-411
- Oliver G. Selfridge:
Atoms of Learning II: Adaptive Strategies A Study of Two-Person Zero-Sum Competition.
412-415
- Terence C. Fogarty:
An Incremental Genetic Algorithm for Real-Time Learning.
416-419
- Ronald R. Yager, Kenneth M. Ford:
Participatory Learning: A Constructivist Model.
420-425
Representational Issues in Machine Learning
Copyright © Tue Feb 9 19:29:58 2010
by Michael Ley (ley@uni-trier.de)
