Multi-class SVM optimization using MCE training with application to topic identification

This paper presents a minimum classification error (MCE) training approach for improving the accuracy of multi-class support vector machine (SVM) classifiers. We have applied this approach to topic identification (topic ID) for human-human telephone conversations from the Fisher corpus using ASR lattice output. The new approach yields improved performance over the traditional techniques for training multi-class SVM classifiers on this task.

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Timothy J. Hazen
• Keywords: ICASSP 2010, Minimum Classification Error, Multi-class Support Vector, Multi-class Svm Classifiers, Signal Processing