Pruning Error Minimization in Least Squares Support Vector Machines  

Oleh:

Kruif, Bas J. de ; Vries, Theo J. A. de

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 3 (May 2003), page 696-702.
Topik: vector; social support; error minimization; least squares; 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

The support vector machine (SVM) is a method for classification and for function approximation. This method commonly makes use of an & epsi ; - insensitive cost function, meaning that errors smaller than & epsi ; remain unpunished. As an alternative, a least squares support vector machine (LSSVM) uses a quadratic cost function. When the LSSVM method is used for function approximation, a non sparse solution is obtained. The sparseness is imposed by pruning, i. e., recursively solving the approximation problem and subsequently omitting data that has a small error in the previous pass. However, omitting data with a small approximation error in the previous pass does not reliably predict what the error will be after the sample has been omitted. In this paper, a procedure is introduced that selects from a data set the training sample that will introduce the smallest approximation error when it will be omitted. It is shown that this pruning scheme outperforms the standard one.

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