Digital Least Squares Support Vector Machines

Authors:
Davide Anguita;Andrea Boni
Affiliations:
Department of Biophysical and Electronic Engineering, University of Genova, Via Opera Pia 11a, 16145 Genova, Italy. e-mail: anguita@dibe.unige.it;Department of Information and Communication Technology, University of Trento, Via Sommarive 14, 38050 Povo (TN), Italy. e-mail: andrea.boni@ing.unitn.it
Venue:
Neural Processing Letters
Year:
2003

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Abstract

This paper presents a very simple digital architecture that implements a Least-Squares Support Vector Machine. The simplicity of the whole system and its good behavior when used to solve classification problems hold good prospects for the application of such a kind of learning machines to build embedded systems.