Relevant Examples and Relevant Features--Thoughts from Computational Learning Theory

Avrim Blum

It can be said that nearly all results in machine learning, whether experimental or theoretical, deal with problems of separating relevant from irrelevant information in some way. In this paper I will attempt to survey some of the results and intuitions developed in the area of computational learning theory. My focus will be on two issues in particular: that some examples may be more relevant than others, and that within an example, some features may be more relevant than others. This survey is by no means even close to comprehensive, and strongly reflects my own personal biases as well as issues brought up by results presented at this workshop.

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