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AN EXTENSION OF A MINIMAX APPROACH TO MULTIPLE CLASSIFICATION

2007
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Journal of the Operations Research Society of Japan
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When mean vectors and covariance matrices of two classes are available in a binary classification problem, Lanckriet et al. [6] propose a minimax approach for finding a linear classifier which minimizes the worst-case (maximum) misclassification probability. In this paper, we extend the minimax approach to a multiple classification problem, where the number m of classes could be more than two. Assume that mean vectors and covariance matrices of all the classes are available, but no further

doi:10.15807/jorsj.50.123
fatcat:fp4gmtnqtzc7dj7psoya64fez4