The Analysis of Decomposition Methods for Support Vector Machines  

Oleh:

Chang, Chih-Chung ; Hsu, Chih-Wei ; Lin, Chih-Jen

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 11 no. 4 (2000), page 1003-1008.
Topik: vector; decomposition methods; support vector machines

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.4 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

The support vector machine (SVM) is a 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. 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.

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