We study the similarity and differences between two state-of-the-art large margin classifiers DWD and SVM, and propose a unified family of classification machines, the FLexible Assortment MachinE (FLAME), where SVM and DWD are two special cases within the family. The FLAME family helps to understand the connection and differences between SVM and DWD method, and also improves both methods by providing a better tradeoff between imbalance sensitivity and high dimensional data piling. Several asymptotic properties of the FLAME classifiers are investigated. Simulations and real data applications are used to illustrate the usefulness.
<a href="http://www.math.binghamton.edu/qiao/">State University of New York at Binghamton</a>
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