By Topic

New Solution Method to Smoothing Support Vector Machine with One Control Parameter Smoothing Function

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)
Baolan Yuan ; Sch. of Inf. Eng., Hangzhou Dianzi Univ., Hangzhou, China ; Wangjun Zhang ; Hao Wu

Support vector machine (SVM) can be seen as, a special binary classification method. The original model is a quadratical programming with linear inequalities constraints. It is a very important issue that how to get the optimal solution of SVM model. In this paper, a new solution method is proposed. The constraints are moved away from the original optimization model by using the approximation solution in the feasible space. One control parameter smoothing function is used to smoothen the objective function of unconstrained model. The smoothing performance is investigated. By theory proof, the proposed unconstrained model has an active performance which can be controlled by one proposed parameter.

Published in:

Intelligent Systems (GCIS), 2010 Second WRI Global Congress on  (Volume:1 )

Date of Conference:

16-17 Dec. 2010