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ORCIDEWSL 1991: Porto, Portugal
- Yves Kodratoff:
Machine Learning - EWSL-91, European Working Session on Learning, Porto, Portugal, March 6-8, 1991, Proceedings. Lecture Notes in Computer Science 482, Springer 1991, ISBN 3-540-53816-X
Part 1: Cronstructive Induction and Multi-strategy Approaches
- Attilio Giordana, Davide Roverso, Lorenza Saitta:
Abstracting Background Knowledge for Concept Learning. 1-13 - Gheorghe Tecuci:
A Multistrategy Learning Approach to Domain Modeling and Knowledge Acquisition. 14-32 - Gerhard Widmer:
Using Plausible Explanations to Bias Empirical Generalizations in Weak Theory Domains. 33-43 - Der-Shung Yang, Gunnar Blix, Larry A. Rendell:
The Replication Problem: A Constructive Induction Approach. 44-61 - F. Zerr, Jean-Gabriel Ganascia:
Integrating an Explanation-Based Learning Mechanism into a General Problem-Solver. 62-80 - Claudio Carpineto:
Analytical Negative Generalization and Empirical Negative Generalization are not Cumulative: A Case Study. 81-88 - Francesco Bergadano
, Floriana Esposito, Céline Rouveirol, Stefan Wrobel:
Panel: Evaluating and Changing Representation in Concept Acquisition. 89-100
Part 2: Discovery
- Jan M. Zytkow, Jieming Zhu:
Panel: Application of Empirical Discovery in Knowledge Acquisition. 101-117 - Majorie Moulet:
Using Accuracy in Scientific Discovery. 118-136 - Gilles Bisson:
KBG: A Generator of Knowledge Bases. 137
Part 3: Numeric and Statistical Approaches
- Bojan Cestnik, Ivan Bratko:
On Estimating Probabilities in Tree Pruning. 138-150 - Peter Clark, Robin Boswell:
Rule Induction with CN2: Some Recent Improvements. 151-163 - Jason Catlett:
On Changing Continuous Attributes into Ordered Discrete Attributes. 164-178 - Floor Verdenius:
A Method for Inductive Cost Optimization. 179-191 - Cullen Schaffer:
When Does Overfitting Decrease Prediction Accuracy in Induced Decision Trees and Rule Sets?. 192-205 - Igor Kononenko:
Semi-Naive Bayesian Classifier. 206-219 - Christine Decaestecker:
Description Contrasting in Incremental Concept Formation. 220-233 - Miroslav Kubat, Jirina Pavlickova:
The System FLORA: Learning from Type-Varying Training Sets. 234 - Marco Dorigo:
Message-Based Bucket Brigade: An Algorithm for the Apportionment of Credit Problem. 235-244
Part 4: Theorem Proving and EBL
- Luc De Raedt, Johan Feyaerts, Maurice Bruynooghe:
Acquiring Object-Knowledge for Learning Systems. 245-264 - Nada Lavrac, Saso Dzeroski, Marko Grobelnik:
Learning Nonrecursive Definitions of Relations with LINUS. 265-281 - Igor Mozetic, Christian Holzbaur:
Extending Explanation-Based Generalization by Abstraction Operators. 282-297 - Catherine Belleannée, Jacques Nicolas:
Static Learning for an Adaptive Theorem Prover. 298-311 - Kai Zercher:
Explanation-Based Generalization and Constraint Propagation with Interval Labels. 312-326 - Paulo Urbano:
Learning by Explanation of Failures. 327-343 - Luc De Raedt:
Panel: Logic and Learnability. 344 - Lorenza Saitta:
Panel: Causality and Learning. 345 - Jacques Nicolas:
Seed Space and Version Space: Generalizing from Approximations. 346 - Sylvain Delisle, Stan Matwin, Lionel Zupan:
Integrating EBL with automatic Text Analysis. 347
Part 5: Inversion of Resolution
- Béatrice Duval:
Abduction and Induction for Explanation-Based Learning. 348-360 - Shan-Hwei Nienhuys-Cheng, Peter A. Flach:
Consistent Term Mappings, Term Partitions and Inverse Resolution. 361-374
Part 6: Analogy and Case-Based Learning
- Manuela M. Veloso, Jaime G. Carbonell:
Learning by Analogical Replay in PRODIGY: First Results. 375-390 - Birgit Tausend, Siegfried Bell:
Analogical Reasoning for Logic Programming. 391-397 - Beatriz López, Enric Plaza:
Case-Based Learning of Strategic Knowledge. 398-411
Part 7: Multi-agents
- Pavel Brazdil, Matjaz Gams, Sati S. Sian, Luís Torgo, Walter Van de Velde:
Panel: Learning in Distributed Systems and Multi-Agent Environments. 412-423 - Pavel Brazdil, Stephen H. Muggleton:
Learning to Relate Terms in a Multiple Agent Environment. 424-439 - Sati S. Sian:
Extending Learning to Multiple Agents: Issues and a Model for Multi-Agent Machine Learning (MA-ML). 440-456
Part 8: Applications
- Peter Clark, Bojan Cestnik, Claude Sammut, Joachim Stender:
Panel: Applications of Machine Learning: Notes from the Panel Members. 457-462 - Hakim Lounis, Gilles Bisson:
Evaluation of Learning Systems: An Artificial Data-Based Approach. 463-481 - Luigi Di Pace, Filippo Fabrocini, Giorgio Bolis:
Shift of Bias in Learning from Drug Compounds: The Fleming Project. 482-493 - Eduardo F. Morales:
Learning Features by Experimentation in Chess. 494-511 - Francis Courtot:
Representation and Induction of Musical Structures for Computer Assisted Composition. 512 - Steffen Schulze-Kremer, Ross D. King:
IPSA: Inductive Protein Structure Analysis. 513 - Thomas R. Addis, Yves Kodratoff, Ramón López de Mántaras, Katharina Morik, Enric Plaza:
Panel: Four Stances on Knowledge Acquisition and Machine Learning. 514-533
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