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Abstract : |
The support vector machine (SVM) is a new and promising technique for pattern recognition. It requires the solution of a large dense quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, very few methods can handle the memory problem and an important one is the "decomposition method. " However, there is no convergence proof so far. In this paper, we connect this method to projected gradient methods and provide theoretical proofs for a version of decomposition methods. An extension to boundconstrained formulation of SVM is also provided., |
