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Abstract:
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Margin-based classifiers have been popular in both machine learning and statistics. Among numerous classifiers, some are hard classifiers and some are soft ones. Soft classifiers explicitly estimate the class conditional probabilities and perform classification based on estimated probabilities. In contrast, hard classifiers directly target on the classification decision boundary without producing the probability estimation. In this talk, instead of making a choice between hard and soft classification, we propose a novel family of large-margin classifiers, namely large-margin unified machines (LUMs), which cover a broad range of margin-based classifiers including both hard and soft ones. By offering a natural bridge from soft to hard classification, the LUM provides a unified algorithm to fit various classifiers. Issues on efficient computation and variable selection will be discussed.
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