Machine Learning, Volume 11

Volume 11, 1993

• Chris S. Wallace, J. D. Patrick: Coding Decision Trees. 7-22 BibTeX
• Sanjeev R. Kulkarni, Sanjoy K. Mitter, John N. Tsitsiklis: Active Learning Using Arbitrary Binary Valued Queries. 23-35 BibTeX
• Yasubumi Sakakibara: Noise-Tolerant Occam Algorithms and Their Applications to Learning Decision Trees. 37-62 BibTeX
• Robert C. Holte: Very Simple Classification Rules Perform Well on Most Commonly Used Datasets. 63-91 BibTeX
• Jan L. Talmon, Herco Fonteijn, Peter J. Braspenning: An Analysis of the WITT Algorithm. 91-104 BibTeX
• Ryszard S. Michalski: Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning. 111-151 BibTeX
• Lorenza Saitta, Marco Botta, Filippo Neri: Multistrategy Learning and Theory Revision. 153-172 BibTeX
• Michael J. Pazzani: Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning. 173-194 BibTeX
• Richard Maclin, Jude W. Shavlik: Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding. 195-215 BibTeX
• Katharina Morik: Balanced Cooperative Modeling. 217-235 BibTeX
• Gheorghe Tecuci: Plausible Justification Trees: A Framework for Deep and Dynamic Integration of Learning Strategies. 237-261 BibTeX