SMO-Based Pruning Methods for Sparse Least Squares Support Vector Machines  

Oleh:

Zeng, Xiangyan ; Chen, Xue-Wen

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 16 no. 6 (Nov. 2005), page 1541-1546.
Topik: machines; SMO - based; sparse; least square; 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

Solutions of least squares support vector machines (LS - SVM s) are typically nonsparse. The sparseness is imposed by subsequently omitting data that introduce the smallest training errors and retraining the remaining data. Iterative retraining requires more intensive computations than training a single nonsparse LS - SVM. In this paper, we propose a new pruning algorithm for sparse LS - SVMs : the sequential minimal optimization (SMO) method is introduced into pruning process ; in addition, instead of determining the pruning points by errors, we omit the data points that will introduce minimum changes to a dual objective function. This new criterion is computationally efficient. The effectiveness of the proposed method in terms of computational cost and classification accuracy is demonstrated by numerical experiments.

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