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How Well do Bayes Methods Work for OnLine Prediction of {±1} values?
 In Proceedings of the Third NEC Symposium on Computation and Cognition. SIAM
, 1992
"... We look at sequential classification and regression problems in which f\Sigma1glabeled instances are given online, one at a time, and for each new instance, before seeing the label, the learning system must either predict the label, or estimate the probability that the label is +1. We look at the ..."
Abstract

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We look at sequential classification and regression problems in which f\Sigma1glabeled instances are given online, one at a time, and for each new instance, before seeing the label, the learning system must either predict the label, or estimate the probability that the label is +1. We look at the performance of Bayes method for this task, as measured by the total number of mistakes for the classification problem, and by the total log loss (or information gain) for the regression problem. Our results are given by comparing the performance of Bayes method to the performance of a hypothetical "omniscient scientist" who is able to use extra information about the labeling process that would not be available in the standard learning protocol. The results show that Bayes methods perform only slightly worse than the omniscient scientist in many cases. These results generalize previous results of Haussler, Kearns and Schapire, and Opper and Haussler. 1 Introduction Several recent papers in...