Support Vecor Machine

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Nonparallel Support Vector Machines for Pattern Classification
Lokesh Sharma sharma.123lokesh@gmail.com Anand Mishra anand.lnmiit@gmail.com Vaibhav Kumar Soni vbhvsoni22@gmail.com Sudhanshu Bansal sudhanshu.bansal@lnmiit.ac.in Prasant Rathore prasant.rathore@lnmiit.ac.in The LNM Institute of Information Technology, Jaipur (INDIA)

Abstract—We introduce a nonparallel classifier knows as nonparllel support vector machine(NPSVM) for the purpose of binary classification. Proposed NPSVM is totally different from the existing non parallel classifier, such as the generalized eigenvalue proximal support vector machine (GEPSVM) and the twin support vector machine (TWSVM). NPSVM has several incomparable advantages:1) Two primal problems are constructed implementing the structural risk minimization principle; 2) The dual problems of these two primal problems have the same advantages as that of the standard SVMs, so that the kernel trick can be applied directly; 3)The dual problems have the same elegant formulation with that of standard SVMs and can certainly be solved efficiently by sequential minimization optimization algorithm, while existing GEPSVM or TWSVMs are not suitable for large scale problems; 4) It has the inherent sparseness as standard SVMs; 5) Existing TWSVMs are only the special cases of the NPSVM when the parameters of which are appropriately chosen. Experimental results on lots of datasets show the effectiveness of our method in both sparseness and classification accuracy, and therefore, confirm the above conclusion further. NPSVM is a new starting point of nonparallel classifiers.

support hyperplanes, have been proposed. In the twin support vector machine (TWSVM), it seeks two nonparallel proximal hyper planes such that each hyper plane is closer to one of the two classes and is at least one distance from the other. This strategy results that…...

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