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- W75165669 abstract "In this paper we present a particular implementation of the Boosting algorithm: its application to the learning of a class of boolean functions. The Boosting algorithm is that proposed by Schapire and Freund, denominated Boosting by sub-sampling. The goal of concept learning, according to the PAC weak model, is to generate a description of another function, called the hypothesis, which is close to the concept, by using a set of examples. The hypotheses which derives from weak learning is Boosted to provide a better hypothesis. Its application concerns the learning of a particular class, the member of the class of boolean functions. First, a weak learning algorithm, trying to learn a class concept with two input Perceptron, was established, and Boosting then applied to improve its learning capacity." @default.
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- W75165669 date "1997-12-01" @default.
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- W75165669 title "The learning of a class of n-dimensional Boolean functions with a two input perceptron using the boosting algorithm" @default.
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