Title :
Creating an ensemble of diverse support vector machines using Adaboost
Author :
Lima, Naiyan Hari Candido ; Neto, Adriao Duarte Doria ; De Melo, Jorge Dantas
Author_Institution :
Dept. of Comput. Eng. & Autom., Univ. Fed. do Rio Grande do Norte, Rio Grande, Brazil
Abstract :
Support vector machines are one of the most employed methods of pattern classification, and the Adaboost algorithm is an effective way of improving the performance of the weak learners that compose the ensemble. In this article, we propose to create an Adaboost-based ensemble of SVM, by altering the Gaussian width parameter of the RBF-SVM. Using data sets from the UCI repository, we made tests to evaluate the algorithm.
Keywords :
Gaussian processes; learning (artificial intelligence); pattern classification; radial basis function networks; support vector machines; Adaboost-based ensemble algorithm; Gaussian width parameter; RBF-SVM; diverse support vector machine learning; pattern classification; Boosting; Diversity reception; Error analysis; Kernel; Machine learning algorithms; Neural networks; Pattern classification; Risk management; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2009.5178915
