A parallel training algorithm for large scale support vector machines
نویسنده
چکیده
Support vector machines (SVMs) are an extremely successful class of classification and regression algorithms. Building an SVM entails the solution of a constrained convex quadratic programming problem which is quadratic in the number of training samples. Previous parallel implementations of SVM solvers sequentially solved subsets of the complete problem, which is problematic when the solution requires many support vectors. In this article we introduce a parallel implementation of a sequential SVM solver which overcomes these problems and makes it possible to solve extremely large SVM problems, with up to several million training points.
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