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Proceedings Paper

An analogue circuit for sequential minimal optimization for support vector machines
Author(s): Matías Jiménez; Horacio Lamela; Jesús Gimeno
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Paper Abstract

In this paper we address the problem of Support Vector Machine (SVM) learning. We describe an analogue implementation for a Sequential Minimal Optimization (SMO) algorithm to simplify the hardware requisites of the learning phase. The advantages from a full set training circuit are shown and a test is carried out on a simple case to prove its effectiveness.

Paper Details

Date Published: 3 April 2008
PDF: 6 pages
Proc. SPIE 6979, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI, 697909 (3 April 2008); doi: 10.1117/12.787474
Show Author Affiliations
Matías Jiménez, Univ. Carlos III de Madrid (Spain)
Horacio Lamela, Univ. Carlos III de Madrid (Spain)
Jesús Gimeno, Univ. Carlos III de Madrid (Spain)


Published in SPIE Proceedings Vol. 6979:
Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI
Harold H. Szu; F. Jack Agee, Editor(s)

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