Zhizheng Liang

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In this paper, an efficient and effective method to solve kernel Fisher discriminant analysis is proposed. Since the QR decomposition on the small-size matrix is adopted, the superiority of the proposed method is its computational efficiency. Moreover, the proposed method can avoid the singularity problem. Most importantly, the proposed method shows that(More)
Choosing multiple hyperparameters for support vector machines has gained wide attention from researchers and this also provides a strategy for automatically selecting scaling factors of features. This paper proposes an efficient feature scaling method for support vector machines with a quadratic kernel. The proposed method alternately performs the standard(More)