Myths and Legends in Learning Classification Rules

Wray Buntine

This paper is a discussion of machine learning theory on empirically learning classification rules. The paper proposes six myths in the machine learning community that address issues of bias, learning as search, computational learning theory, Occam’s razor, "universal" learning algorithms, and interactive learning. Some of the problems raised are also nddrcssed from a Bayesian perspective. The paper concludes by suggesting questions that machine learning researchers should be addressing both theoretically and experimentally.


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