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- W1483669989 abstract "We look at sequential classification and regression problems in which {+- 1}-labeled instances are given on-line, 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 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." @default.
- W1483669989 created "2016-06-24" @default.
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- W1483669989 date "1992-07-01" @default.
- W1483669989 modified "2023-09-24" @default.
- W1483669989 title "HOW WELL DO BAYES METHODS WORK FOR ON-LINE PREDICTION OF {+- 1} VALUES?" @default.
- W1483669989 hasPublicationYear "1992" @default.
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