The analysis of decomposition methods for support vector machines
Resource
IEEE Transactions on Neural Networks 11 (4): 1003-1008
Journal
IEEE Transactions on Neural Networks
Journal Volume
11
Journal Issue
4
Pages
1003-1008
Date Issued
2000
Author(s)
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 bound-constrained formulation of SVM is also provided.We then show that this convergence proof is valid for general decomposition methods if their working set selection meets a simple requirement.
Type
journal article
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