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Learning algorithm for nonlinear support vector machines suited for digital VLSI

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3 Author(s)
D. Anguita ; Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy ; A. Boni ; S. Ridella

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

Published in:

Electronics Letters  (Volume:35 ,  Issue: 16 )