An Error Bound Based on a Worst Likely Assignment
Eric T. Bax, Augusto Callejas · 2008
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.