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Efficient Minimax Clustering Probability Machine by Generalized Probability Product Kernel
2008
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
Minimax Probability Machine (MPM), learning a decision function by minimizing the maximum probability of misclassification, has demonstrated very promising performance in classification and regression. However, MPM is often challenged for its slow training and test procedures. Aiming to solve this problem, we propose an efficient model named Minimax Clustering Probability Machine (MCPM). Following many traditional methods, we represent training data points by several clusters. Different from
doi:10.1109/ijcnn.2008.4634375
dblp:conf/ijcnn/YangHKL08
fatcat:tyev3euutjb3plrnjtfttbuvge