Pattern Classiffication using SVM with GMM Data Selection Training Methode

Abstract

In pattern recognition, support vector machines (SVM) as a discriminative classifier and Gaussian mixture model as a generative model classifier are two most popular techniques. Current state-of-the-art systems try to combine them together for achieving more power of classification and improving the performance of the recognition systems. Most of recent works focus on probabilistic SVM/GMM hybrid methods but this paper presents a novel method for SVM/GMM hybrid pattern classification based on training data selection. This system uses the output of the Gaussian mixture model to choose training data for SVM classifier. Results on databases are provided to demonstrate the effectiveness of this system. We are able to achieve better error-rates that are better than the current systems.