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Semantic Scholar6. 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
- Raymond J. Mooney, Dirk Ourston:
Induction Over the Unexplained: Integrated Learning of Concepts with Both Explainable and Conventional Aspects. ML 1989: 5-7 - 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 - Andrea Pohoreckyj Danyluk:
Finding New Rules for Incomplete Theories: Explicit Biases for Induction with Contextual Information. ML 1989: 34-36 - 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 - Gerhard Friedrich, Wolfgang Nejdl:
Using Domain Knowledge to Improve Inductive Learning Algorithms for Diagnosis. ML 1989: 75-77 - 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 - Jude W. Shavlik, Geoffrey G. Towell:
Combining Explanation-Based Learning and Artificial Neural Networks. ML 1989: 90-93
Empirical Learning: Theory and Application
- Matjaz Gams, Aram Karalic:
New Empirical Learning Mechanisms Perform Significantly Better in Real Life Domains. ML 1989: 99-103 - Stephen 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 - Rodney M. Goodman, Padhraic Smyth:
The Induction of Probabilistic Rule Sets - The Itrule Algorithm. ML 1989: 129-132 - 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 - 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 - 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
Learning Plan Knowledge
- 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 - 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 - 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 - 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 - Timothy M. Converse, Kristian J. Hammond, Mitchell Marks:
Learning from Opportunity. ML 1989: 246-248 - Melinda T. Gervasio, Gerald DeJong:
Explanation-Based Learning of Reactive Operations. ML 1989: 252-254
Knowledge-Based Refinement and Theory Revision
- 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 - 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 - 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 - 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 - 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 - Scott H. Clearwater, Tze-Pin Chen, Haym Hirsh, Bruce G. Buchanan:
Incremental Batch Learning. ML 1989: 366-370 - 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 - 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 - 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
Representational Issues in Machine Learning
- 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 - Robert C. Holte, Robert M. Zimmer:
A Mathematical Framework for Studying Representation. ML 1989: 454-456 - Larry A. Rendell:
Comparing Systems and analyzing Functions to Improve Constructive Induction. ML 1989: 461-464 - Mieczyslaw M. Kokar:
Concept Discovery Through Utilization of Invariance Embedded in the Description Language. ML 1989: 478-479 - Sunil Mohan, Chris Tong:
Automatic Construction of a Hierarchical Generate-and-Test Algorithm. ML 1989: 483-484 - 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
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last updated on 2018-04-19 18:59 CEST by the dblp team