Radial basis function kernel optimization for Support Vector Machine classifiers

2020/07/16 by Karl Thurnhofer‐Hemsi, Karl Thurnhofer-Hemsi, Ezequiel López-Rubio +8 · 3 citations
Computer Science · Mathematics · #Face and Expression Recognition #Machine Learning and Data Classification #Video Surveillance and Tracking Methods #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.2007.08233

9 pages, 5 figures, 1 table (main paper), 8 pages, 6 figures, 2 tables (supplementary material). To be submitted to IEEE Transactions on Neural Networks and Learning Systems

arxiv created 2020/07/16 · arxiv updated 2020/07/17

Abstract

Support Vector Machines (SVMs) are still one of the most popular and precise classifiers. The Radial Basis Function (RBF) kernel has been used in SVMs to separate among classes with considerable success. However, there is an intrinsic dependence on the initial value of the kernel hyperparameter. In this work, we propose OKSVM, an algorithm that automatically learns the RBF kernel hyperparameter and adjusts the SVM weights simultaneously. The proposed optimization technique is based on a gradient descent method. We analyze the performance of our approach with respect to the classical SVM for classification on synthetic and real data. Experimental results show that OKSVM performs better irrespective of the initial values of the RBF hyperparameter.

Cited by

• From Pen to Palette: Mathematical Analysis of van Gogh's Psychological Trajectory
• Intersectional Fairness in Vision-Language Models for Medical Image Disease Classification
• Beyond the Gegenbauer Paradigm: q-Orthogonal Kernels for Machine Learning

Related

• Cluster based RBF Kernel for Support Vector Machines
• Compressed Support Vector Machines
• How to tune the RBF SVM hyperparameters?: An empirical evaluation of 18 search algorithms
• A Novel Robust Optimization Model for Nonlinear Support Vector Machine
• Fast Prediction with SVM Models Containing RBF Kernels