Learning algorithm for nonlinear support vector machines suited for digital VLSI

被引：17
|
作者：
Anguita, D [1 ]
Boni, A [1 ]
Ridella, S [1 ]
机构：
[1] Univ Genoa, Dept Biophys & Elect Engn, I-16145 Genoa, Italy
来源：
ELECTRONICS LETTERS | 1999年 / 35卷 / 16期
关键词：
D O I：
10.1049/el:19990950
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A learning algorithm for radial basis function support vector machines (RBF-SVMs) that can be easily implemented in digital VLSI is proposed. It is shown that, as opposed to traditional artificial neural networks, learning in SVMs is very robust with respect to quantisation effects deriving from the finite precision of computations.
引用
收藏
页码：1349 / 1350
页数：2
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