Incremental Training of Support Vector Machines  

Oleh:

Shilton, A. ; Palaniswami, M. ; Ralph, D. ; Ah, Chung Tsoi

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 16 no. 1 (Jan. 2005), page 114-131.
Topik: support; incremental training; support vector; machines

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.12 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We propose a new algorithm for the incremental training of support vector machines (SVMs) that is suitable for problems of sequentially arriving data and fast constraint parameter variation. Our method involves using a "warm - start" algorithm for the training of SVM s, which allows us to take advantage of the natural incremental properties of the standard active set approach to linearly constrained optimization problems. Incremental training involves quickly retraining a support vector machine after adding a small number of additional training vectors to the training set of an existing (trained) support vector machine. Similarly, the problem of fast constraint parameter variation involves quickly retraining an existing support vector machine using the same training set but different constraint parameters. In both cases, we demonstrate the computational superiority of incremental training over the usual batch retraining method.

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