Hidden Space Support Vector Machines  

Oleh:

Zhang, Li ; Zhou, Weida ; Jiao, Licheng

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 15 no. 6 (Nov. 2004), page 1424-1434.
Topik: support; hidden space; support vector; machines

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.11 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Hidden space support vector machines (HSSVM s) are presented in this paper. The input patterns are mapped into a high - dimensional hidden space by a set of hidden nonlinear functions and then the structural risk is introduced into the hidden space to construct HSSVM s. Moreover, the conditions for the nonlinear kernel function in HSSVM s are more relaxed, and even differentiability is not required. Compared with support vector machines (SVM s), HSSVM s can adopt more kinds of kernel functions because the positive definite property of the kernel function is not a necessary condition. The performance of HSSVM s for pattern recognition and regression estimation is also analyzed. Experiments on artificial and real - world domains confirm the feasibility and the validity of our algorithms.

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