ML Anthology

MLJ 1993

41 papers

A Knowledge-Intensive Genetic Algorithm for Supervised Learning Cezary Z. Janikow
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A Reply to Cohen's Book Review of Creating a Memory of Causal Relationships Michael J. Pazzani
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A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features R. Scott Cost, Steven Salzberg
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Active Learning Using Arbitrary Binary Valued Queries Sanjeev R. Kulkarni, Sanjoy K. Mitter, John N. Tsitsiklis
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An Analysis of the WITT Algorithm Jan L. Talmon, Herco Fonteijn, Peter J. Braspenning
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An Integrated Framework for Empirical Discovery Bernd Nordhausen, Pat Langley
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Balanced Cooperative Modeling Katharina Morik
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Bivariate Scientific Function Finding in a Sampled, Real-Data Testbed Cullen Schaffer
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Coding Decision Trees Chris S. Wallace, Jon D. Patrick
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Competition-Based Induction of Decision Models from Examples David Perry Greene, Stephen F. Smith
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Cost-Sensitive Learning of Classification Knowledge and Its Applications in Robotics Ming Tan
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Derivational Analogy in Prodigy: Automating Case Acquisition, Storage, and Utilization Manuela M. Veloso, Jaime G. Carbonell
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Design Methods for Scientific Hypothesis Formation and Their Application to Molecular Biology Peter D. Karp
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Discovery as Autonomous Learning from the Environment Wei-Min Shen
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Discovery by Minimal Length Encoding: A Case Study in Molecular Evolution Aleksandar Milosavljevic, Jerzy Jurka
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Experience Selection and Problem Choice in an Exploratory Learning System Paul D. Scott, Shaul Markovitch
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Explanation-Based Learning for Diagnosis Yousri El Fattah, Paul O'Rorke
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Extracting Refined Rules from Knowledge-Based Neural Networks Geoffrey G. Towell, Jude W. Shavlik
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Genetic Reinforcement Learning for Neurocontrol Problems L. Darrell Whitley
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Indexing and Elaboration and Refinement: Incremental Learning of Explanatory Cases Ashwin Ram
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Induction over the Unexplained: Using Overly-General Domain Theories to Aid Concept Learning Raymond J. Mooney
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Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Ryszard S. Michalski
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Information Filtering: Selection Mechanisms in Learning Systems Shaul Markovitch, Paul D. Scott
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Integrating Feature Extraction and Memory Search Christopher C. Owens
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Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning Michael J. Pazzani
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Machine Discovery of Effective Admissible Heuristics Armand Prieditis
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Multistrategy Learning and Theory Revision Lorenza Saitta, Marco Botta, Filippo Neri
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Noise-Tolerant Occam Algorithms and Their Applications to Learning Decision Trees Yasubumi Sakakibara
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Opportunism and Learning Kristian J. Hammond, Timothy M. Converse, Mitchell Marks, Colleen M. Seifert
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Overfitting Avoidance as Bias Cullen Schaffer
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Plausible Justification Trees: A Framework for Deep and Dynamic Integration of Learning Strategies Gheorghe Tecuci
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Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time Andrew W. Moore, Christopher G. Atkeson
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Research Note on Decision Lists Ron Kohavi, Scott Benson
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Selecting a Classification Method by Cross-Validation Cullen Schaffer
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Synthesis of UNIX Programs Using Derivational Analogy Sanjay Bhansali, Mehdi T. Harandi
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The Design of Discrimination Experiments Shankar A. Rajamoney
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Using Genetic Algorithms for Concept Learning Kenneth A. De Jong, William M. Spears, Diana F. Gordon
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Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding Richard Maclin, Jude W. Shavlik
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Very Simple Classification Rules Perform Well on Most Commonly Used Datasets Robert C. Holte
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Wastewater Treatment Systems from Case-Based Reasoning Srinivas Krovvidy, William G. Wee
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What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation Stephanie Forrest, Melanie Mitchell
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