Titlebook: Machine Learning; An Artificial Intell Ryszard S. Michalski (Professor of Computer Scienc Book 1983 Springer-Verlag Berlin Heidelberg 1983

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Book 1983learn­ ing processes is of great significance to fields concerned with understanding in­ telligence. Such fields include cognitive science, artificial intelligence, infor­ mation science, pattern recognition, psychology, education, epistemology, philosophy, and related disciplines. The recent observ

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内部

A Theory and Methodology of Inductive Learningckground knowledge, and guided by criteria evaluating the “quality” of generated inductive assertions..Based on this theory, a general methodology for learning structural descriptions from examples, called ., is described and illustrated by a problem from the area of conceptual data analysis.

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Why Should Machines Learn?ith machine learning. Now although we all believe machines can simulate human thought—unless we’re vitalists, and there aren’t any of those around any more—still, I didn’t think that was what was intended by the title of the book. I didn’t think it was appropriate to write about psychology.

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Using Proofs and Refutations to Learn from Experienceeory fix produces as a by-product new domain concepts that capture environmental characteristics of instrumental value to the learner. The techniques proposed here provide the first analytical methods for constructing new knowledge. They extend and make practical the ideas of proofs and refutations originally introduced by Lakatos.

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Rediscovering Chemistry with the Bacon Systemt Proust’s law of definite proportions, Gay-Lussac’s law of combining volumes, Cannizzaro’s determination of the relative atomic weights, and Prout’s hypothesis. The BACON.4 heuristics, including the new technique for finding common divisors, appear to be general mechanisms applicable to discovery in diverse domains.

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