Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Successive overrelaxation for support vector machines
IEEE Transactions on Neural Networks
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Support vector regression (SVR) is an important tool for data mining. In this paper, we first introduce a new way to make SVR have the similar mathematic form as that of support vector classification. Then we propose a versatile iterative method, successive overrelaxation, for the solution of extremely large regression problems using support vector machines. Experiments prove that this new method converges considerably faster than other methods that require the presence of a substantial amount of the data in memory.