By Topic

SVM learning from large training data set

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

4 Author(s)
Murphey, Y.L. ; Dept. of Electr. & Comput. Eng., Michigan Univ., Dearborn, MI, USA ; Zhihang Chen ; Putrus, M. ; Feldkamp, L.

Support vector machines have been gaining popularity in the research community of pattern classification. In this paper, we investigate efficient and effective algorithms for training SVMs on large data collections. We decompose SVM learning problem into two stages. At the first stage we developed an algorithm that uses a sequence of small subsets of training data to select the parameters γ and C. At the second stage, we developed an algorithm that generates a reliable set of support vectors using a small subset of the training data. Experiments are conducted on about 850K data samples for automotive engine misfire detection.

Published in:

Neural Networks, 2003. Proceedings of the International Joint Conference on  (Volume:4 )

Date of Conference:

20-24 July 2003