By Topic

A New Solution Path Algorithm in Support Vector Regression

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)
Gang Wang ; Hong Kong Univ. of Sci. & Technol., Kowloon ; Dit-Yan Yeung ; Lochovsky, F.H.

In this paper, regularization path algorithms were proposed as a novel approach to the model selection problem by exploring the path of possibly all solutions with respect to some regularization hyperparameter in an efficient way. This approach was later extended to a support vector regression (SVR) model called epsiv -SVR. However, the method requires that the error parameter epsiv be set a priori. This is only possible if the desired accuracy of the approximation can be specified in advance. In this paper, we analyze the solution space for epsiv-SVR and propose a new solution path algorithm, called epsiv-path algorithm, which traces the solution path with respect to the hyperparameter epsiv rather than lambda. Although both two solution path algorithms possess the desirable piecewise linearity property, our epsiv-path algorithm overcomes some limitations of the original lambda-path algorithm and has more advantages. It is thus more appealing for practical use.

Published in:

Neural Networks, IEEE Transactions on  (Volume:19 ,  Issue: 10 )