Fast Modular Network Implementation for Support Vector Machines  

Oleh:

Huang, Guang-Bin ; Mao, K. Z. ; Siew, Chee-Kheong ; Huang, De-Shuang

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 16 no. 6 (Nov. 2005), page 1651-1663.
Topik: modularity; fast modular; network implementation; support vector machines

Ketersediaan

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

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Isi artikel

Support vector machines (SVM s) have been extensively used. However, it is known that SVM s face difficulty in solving large complex problems due to the intensive computation involved in their training algorithms, which are at least quadratic with respect to the number of training examples. This paper proposes a new, simple, and efficient network architecture which consists of several SVM s each trained on a small subregion of the whole data sampling space and the same number of simple neural quantizer modules which inhibit the outputs of all the remote SVM s and only allow a single local SVM to fire (produce actual output) at any time. In principle, this region - computing based modular network method can significantly reduce the learning time of SVM algorithms without sacrificing much generalization performance. The experiments on a few real large complex benchmark problems demonstrate that our method can be significantly faster than single SVM s without losing much generalization performance.

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