Abstract
In order to get good hyperparameters of SVM, user needs to conduct extensive cross-validation such as leave-one-out (LOO) cross-validation. Alpha seeding is often used to reduce the cost of SVM training. Compared with the existing schemes of alpha seeding, a new efficient alpha seeding method is proposed. Through some examples, its good performance has been proved. Interpretation from both geometrical and mathematical view is also given.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Lee, J.H., Lin, C.J.: Automatic model selection for support vector machines, Dept. Comput. Sci., Inform. Eng., National Taiwan Univ., Taipei, Tech. Rep. (2000)
DeCoste, D., Wagstaff, K.: Alpha seeding for support vector machines. In: Int. Conf. Knowledge Discovery and Data Mining (2000)
Lee, M.S., Kerrthi, S.S., Ong, C.J.: An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels. IEEE Trancs. Neural Networks 5, 750–757 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Feng, D., Shi, W., Guo, H., Chen, L. (2005). A New Alpha Seeding Method for Support Vector Machine Training. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_87
Download citation
DOI: https://doi.org/10.1007/11539087_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
eBook Packages: Computer ScienceComputer Science (R0)