Cross kernel distance minimization for designing support vector machines

Cross kernel distance minimization for designing support vector machines Cross distance minimization algorithm (CDMA) is an iterative method for designing a hard margin linear SVM based on the nearest point pair between the convex hulls of two linearly separable data sets. In this paper, we propose a new version of CDMA with clear explanation of its linear time complexity. Using kernel function and quadratic cost, we extend the new CDMA to its kernel version, namely, the cross kernel distance minimization algorithm (CKDMA), which has the requirement of linear memory storage and the advantages over the CDMA including: (1) it is applicable in the non-linear case; (2) it allows violations to classify non-separable data sets. In terms of testing accuracy, training time, and number of support vectors, experimental results show that the CKDMA is very competitive with some well-known and powerful SVM methods such as nearest point algorithm (NPA), kernel Schlesinger-Kozinec (KSK) algorithm and sequential minimal optimization (SMO) algorithm implemented in LIBSVM2.9. International Journal of Machine Learning and Cybernetics Springer Journals

Cross kernel distance minimization for designing support vector machines

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Springer Berlin Heidelberg
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Complex Systems; Systems Biology; Pattern Recognition
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