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Statistical Mechanics of Learning in the Presence of Outliers

Rainer Dietrich·Manfred Opper
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Abstract

Using methods of statistical mechanics, we analyse the effect of outliers on the supervised learning of a classification problem. The learning strategy aims at selecting informative examples and discarding outliers. We compare two algorithms which perform the selection either in a soft or a hard way. When the fraction of outliers grows large, the estimation errors undergo a first order phase transition.

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Rainer Dietrich, Manfred Opper. 1999-02-25. Statistical Mechanics of Learning in the Presence of Outliers. https://doi.org/10.1088/0305-4470%2F31%2F46%2F005

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