Sifting Informative Examples from a Random Source

Yoav Freund

We discuss two types of algorithms for selecting relevant examples that have been developed in the context of computation learning theory. The examples are selected out of a stream of examples that are generated independently at random. The first two algorithms are the so-called "boosting" algorithms of Sehapire [Schapire, 1990] and Freund [Freund, 1990], and the Query-by-Committee algorithm of Seung [Seung et al., 1992]. We describe the algorithms and some of their proven properties, point to some of their commonalities, and suggest some possible future implications.


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