Study and evaluation of a multi-class SVM classifier using diminishing learning technique

Support vector machine (SVM) is one of the state-of-the-art tools for linear and non-linear pattern classification. One of the design objectives of an SVM classifier is reducing the number of support vectors without compromising the classification accuracy. For this purpose, a novel technique referred to as diminishing learning (DL) technique is proposed in this paper for a multiclass SVM classifier. In this technique, a sequential classifier is proposed wherein the classes which require stringent boundaries are tested one by one and once the tests for these classes fail, the stringency of the classifier is increasingly relaxed. An automated procedure is also proposed to obtain the optimum classification order for SVM-DL classifier in order to improve the recognition accuracy. The proposed technique is applied for SVM based isolated digit recognition system and is studied using speaker dependent and multispeaker dependent TI46 database of isolated digits. Both LPC and MFCC are used for feature extraction. The features extracted are mapped using self-organized feature maps (SOFM) for dimensionality reduction and the mapped features are used by SVM classifier to evaluate the recognition accuracy using various kernels. The performance of the system using the proposed SVM-DL classifier is compared with those using other techniques: one-against-all (OAA), half-against-half (HAH) and directed acyclic graph (DAG). SVM-DL classifier results in 1-2% increase in recognition accuracy compared to HAH classifier for some of the kernels with both LPC and MFCC feature inputs. For MFCC feature inputs, both HAH and SVM-DL classifiers have 100% recognition accuracy for some of the kernels. The total number of support vectors required is the least for HAH classifier followed by the SVM-DL classifier. The proposed diminishing learning technique is applicable for a number of pattern recognition applications. (C) 2010 Elsevier B.V. All rights reserved.