A new alpha seeding method for support vector machine training

Authors:
Du Feng;Wenkang Shi;Huawei Guo;Liangzhou Chen
Affiliations:
School of Electronics & Electrics Engineering, Shanghai Jiao Tong University, Shanghai, China;School of Electronics & Electrics Engineering, Shanghai Jiao Tong University, Shanghai, China;School of Electronics & Electrics Engineering, Shanghai Jiao Tong University, Shanghai, China;School of Electronics & Electrics Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

Citing 2
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Alpha seeding for support vector machines

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An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels

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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.