A Survey on SVM Hyper-Parameters Optimization Techniques-Reference-Cited by-同舟云学术

A Survey on SVM Hyper-Parameters Optimization Techniques

Published:2020 Issue: Volume: Page:243-256
ISSN:2367-3370
Container-title:Social Networking and Computational Intelligence
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Author:

Kalita Dhruba Jyoti,Singh Vibhav Prakash,Kumar Vinay

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-15-2071-6_20

Reference23 articles.

1. Vapnik V (1995) The nature of statistical learning theory. Springer New York Google Scholar

2. Bousquet, O, Elisseeff A (2002) Stability and generalization. J Mach Learn Res 2:499–526

3. Vapnik V, Chapelle O (2000) Bounds on error expectation for support vector machines. Neural Comput 12(9):2013–2036

4. Do H, Kalousis A (2013) Convex formulations of radius-margin based support vector machines. In: International conference on machine learning, pp 169–177

5. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge

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