Cascading Asymmetric Linear Classifiers
نویسنده
چکیده
Motivation: Combinations of classifiers have been found useful empirically, yet there is no formal proof of their generalization ability. Our goal is to develop an algorithm to train a sequence of linear classifiers yielding a nonlinear decision surface. We believe that choosing asymmetric regularization parameters for each class can yield a sequence of classifiers that approximates arbitrarily the bayes error, modulo the density of the sampling.
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تاریخ انتشار 2002