Silicon support vector machine with on-line learning
Training of support vector machines (SVMs) amounts to solving a quadratic programming problem over the training data. We present a simple on-line SVM training algorithm of complexity approximately linear in the number of training vectors, and linear in the number of support vectors. The algorithm implements an on-line variant of sequential minimum optimization (SMO) that avoids the need for adjusting select pairs of training coefficients by adjusting the bias term along with the coefficient of the currently presented training vector. The coefficient assignment is a function of the margin returned by the SVM classifier prior to assignment, subject to inequality constraints. The training scheme lends efficiently to dedicated SVM hardware for real-time pattern recognition, implemented using resources already provided for run-time operation. Performance gains are illustrated using the Kerneltron, a massively parallel mixed-signal VLSI processor for kernel-based real-time video recognition.
support vector machine (svm) sequential minimum optimization (smo) pattern recognition large margin classifiers quadratic programming (qp) matrix-vector multiplication (mvm) kernel machines analog array processors