Fusing sequential minimal optimization and Newton’s method for support vector training
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Link
http://link.springer.com/content/pdf/10.1007/s13042-014-0265-x.pdf
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4. He Q, Wu C (2011) Separating theorem of samples in Banach space for support vector machine learning. Int J Mach Learn Cybernet 2(1):49–54
5. Wang X, Lu S, Zhai J (2008) Fast fuzzy multi-category SVM based on support vector domain description. Int J Pattern Recogn Artif Intell 22(1):109–120
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