Reducing the run-time complexity of support vector machines
The Support Vector Machine (SVM) is a new and promising technique for classification and regression, developed by V. Vapnik and his group at AT&T Bell Labs [2, 9]. The technique can be seen as a new training algorithm for Polynomial, Radial Basis Function and Multi-Layer Perceptron networks. SVMs are currently considered slower at runtime than other techniques with similar generalization performance. In this paper we focus on SVM for classification and investigate the problem of reducing its...