Classification in A Normalized Feature Space Using Support Vector Machines  

Oleh:

Borer, S. ; Graf, A. B. A. ; Smola, A. J.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 3 (May 2003), page 597-605.
Topik: vectors; social support; classification; normalized feature; space; support vector machines

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.7 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

This paper discusses classification using support vector machines in a normalized feature space. We consider both normalization in input space and in feature space. Exploiting the fact that in this setting all points lie on the surface of a unit hypersphere we replace the optimal separating hyperplane by one that is symmetric in its angles, leading to an improved estimator. Evaluation of these considerations is done in numerical experiments on two real - world data sets. The stability to noise of this offset correction is subsequently investigated as well as its optimality.

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