2021/06/02 by Pritam Anand, Anand, Pritam, Reshma Rastogi +3
Computer Science · Mathematics · #Face and Expression Recognition #Machine Learning and Algorithms #Neural Networks and Applications #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2106.01109
The numerical results presented in this paper can be regenerated by the code available at https://github.com/ltpritamanand/UnifiedPinSVM/ . We hope that our this work will let the researchers to use the correct formulation of Pin-SVM model in future and improve the predictions across different domain of technologies
arxiv created 2021/06/02 · arxiv updated 2021/06/03
Recently, there have been several papers that discuss the extension of the Pinball loss Support Vector Machine (Pin-SVM) model, originally proposed by Huang et al.,[1][2]. Pin-SVM classifier deals with the pinball loss function, which has been defined in terms of the parameter τ. The parameter τ can take values in [ -1,1]. The existing Pin-SVM model requires to solve the same optimization problem for all values of τ in [ -1,1]. In this paper, we improve the existing Pin-SVM model for the binary classification task. At first, we note that there is major difficulty in Pin-SVM model (Huang et al. [1]) for -1 ≤ τ< 0. Specifically, we show that the Pin-SVM model requires the solution of different optimization problem for -1 ≤ τ< 0. We further propose a unified model termed as Unified Pin-SVM which results in a QPP valid for all -1≤ τ≤ 1 and hence more convenient to use. The proposed Unified Pin-SVM model can obtain a significant improvement in accuracy over the existing Pin-SVM model which has also been empirically justified by extensive numerical experiments with real-world datasets.