Learning a powerful SVM using piece-wise linear loss functions
This work provides an incremental improvement for machine learning practitioners by generalizing existing SVM models through adaptive loss functions.
This paper introduces k-Piece-wise Linear loss Support Vector Machine (k-PL-SVM) models, which use general k-piece-wise linear convex loss functions to measure empirical risk. The k-PL-SVM models are adaptive and can learn a suitable loss function based on the training data, demonstrating improvement over existing SVM models in numerical experiments for k=2 and 3.
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.