An analogue circuit for sequential minimal optimization for support vector machines

Matías Jiménez; Horacio Lamela; Jesús Gimeno

doi:10.1117/12.787474

3 April 2008 An analogue circuit for sequential minimal optimization for support vector machines

Matías Jiménez, Horacio Lamela, Jesús Gimeno

Proceedings Volume 6979, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI; 697909 (2008) https://doi.org/10.1117/12.787474
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

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.

Citation Download Citation

Matías Jiménez, Horacio Lamela, and Jesús Gimeno "An analogue circuit for sequential minimal optimization for support vector machines", Proc. SPIE 6979, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI, 697909 (3 April 2008); https://doi.org/10.1117/12.787474

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