An Error Bound Based on a Worst Likely Assignment

Eric Bax, Augusto Callejas.

Year: 2008, Volume: 9, Issue: 28, Pages: 859−891


Abstract

This paper introduces a new PAC transductive error bound for classification. The method uses information from the training examples and inputs of working examples to develop a set of likely assignments to outputs of the working examples. A likely assignment with maximum error determines the bound. The method is very effective for small data sets.

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