3 April 2008 An analogue circuit for sequential minimal optimization for support vector machines
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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.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Matías Jiménez, Matías Jiménez, Horacio Lamela, Horacio Lamela, Jesús Gimeno, 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); doi: 10.1117/12.787474; https://doi.org/10.1117/12.787474


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