By Topic

Efficient Algorithm for Localized Support Vector Machine

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Haibin Cheng ; Yahoo! Labs., Santa Clara, CA, USA ; Pang-Ning Tan ; Rong Jin

This paper presents a framework called localized support vector machine (LSVM) for classifying data with nonlinear decision surfaces. Instead of building a sophisticated global model from the training data, LSVM constructs multiple linear SVMs, each of which is designed to accurately classify a given test example. A major limitation of this framework is its high computational cost since a unique model must be constructed for each test example. To overcome this limitation, we propose an efficient implementation of LSVM, termed profile SVM (PSVM). PSVM partitions the training examples into clusters and builds a separate linear SVM model for each cluster. Our empirical results show that (1) LSVM and PSVM outperform nonlinear SVM for all 20 of the evaluated data sets and (2) PSVM achieves comparable performance as LSVM in terms of model accuracy but with significant computational savings. We also demonstrate the efficacy of the proposed approaches in terms of classifying data with spatial and temporal dependencies.

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:22 ,  Issue: 4 )