Learning a powerful SVM using piece-wise linear loss functions

• URL: http://arxiv.org/abs/2102.04849v1
• Date: Tue, 9 Feb 2021 14:45:08 GMT
• Title: Learning a powerful SVM using piece-wise linear loss functions
• Authors: Pritam Anand
• Abstract summary: k-Piece-wise Linear loss Support Vector Machine (k-PL-SVM) model is an adaptive SVM model. We have performed the extensive numerical experiments with k-PL-SVM models for k = 2 and 3.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we have considered general k-piece-wise linear convex loss functions in SVM model for measuring the empirical risk. The resulting k-Piece-wise Linear loss Support Vector Machine (k-PL-SVM) model is an adaptive SVM model which can learn a suitable piece-wise linear loss function according to nature of the given training set. The k-PL-SVM models are general SVM models and existing popular SVM models, like C-SVM, LS-SVM and Pin-SVM models, are their particular cases. We have performed the extensive numerical experiments with k-PL-SVM models for k = 2 and 3 and shown that they are improvement over existing SVM models.

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