Title :
A new ensemble learning with support vector machines
Author :
Debnath, Rameswar ; Takahashi, Haruhisa
Author_Institution :
Dept. of Inf., Univ. of Electro-Commun., Chofu, Japan
Abstract :
Cascade of classifiers can, in general, improve the performance of any given classifier. In this paper, we present a new cascade classifier constructed with the support vector machine (SVM) classifiers where a set of SVMs is learned repeatedly with the bounded support vectors of the previous SVM. A binary decision tree is formed using the learned classifiers to take the decision of a new example. Experimental results show that the proposed method can improve the generalization performance over a single SVM.
Keywords :
decision trees; learning (artificial intelligence); pattern classification; support vector machines; SVM classifiers; binary decision tree; cascade classifier; ensemble learning; generalization performance; learned classifiers; support vector machines; support vectors; Boosting; Decision trees; Machine learning algorithms; Support vector machines; Training; Training data; binary decision tree; boosting learning; support vector machine;
Conference_Titel :
Computer and Information Application (ICCIA), 2010 International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-1-4244-8597-0
DOI :
10.1109/ICCIA.2010.6141529
