A sparse, positive-definite kernel function is adopted as the basis for the formulation of a simple support vector machine (SVM) model in which the bias term is removed. The resulting functional to be minimised in the SVM training process is shown to be equivalent to the potential function (co-content) of a linear, resistive circuit featuring low complexity
Low-complexity, linear circuit implementation of Support Vector Machines training
GASTALDO, PAOLO;PARODI, MAURO;ZUNINO, RODOLFO
2008-01-01
Abstract
A sparse, positive-definite kernel function is adopted as the basis for the formulation of a simple support vector machine (SVM) model in which the bias term is removed. The resulting functional to be minimised in the SVM training process is shown to be equivalent to the potential function (co-content) of a linear, resistive circuit featuring low complexity
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