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Introduction: Paradigms for Machine Learning

One can identify four major paradigms under active investigation: inductive learning, analytic learning, genetic algorithms, and connectionist learning methods

Jaime G. Carbonell | J. Carbonell

[1] Tom Michael Mitchell. Version spaces: an approach to concept learning. , 1979 .

[2] Richard Fikes,et al. STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.

[3] J. Ross Quinlan,et al. Learning Efficient Classification Procedures and Their Application to Chess End Games , 1983 .

[4] Arthur L. Samuel,et al. Some studies in machine learning using the game of checkers" in computers and thought eds , 1995 .

[5] Oren Etzioni,et al. Acquiring Effective Search Control Rules: Explanation-Based Learning in the PRODIGY System , 1987 .

[6] R. Michalski,et al. Learning from Observation: Conceptual Clustering , 1983 .

[7] John H. Holland,et al. Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems , 1995 .

[8] Patrick Henry Winston,et al. Learning structural descriptions from examples , 1970 .