Title :
Learning algorithm for nonlinear support vector machines suited for digital VLSI
Author :
Anguita, D. ; Boni, A. ; Ridella, S.
Author_Institution :
Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy
fDate :
8/5/1999 12:00:00 AM
Abstract :
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
Keywords :
VLSI; digital integrated circuits; learning (artificial intelligence); neural chips; radial basis function networks; artificial neural network; digital VLSI; learning algorithm; nonlinear support vector machine; quantisation; radial basis function network;
Journal_Title :
Electronics Letters
DOI :
10.1049/el:19990950
