Pre-extracting boundary vectors for support vector machine using pseudo-density estimation method

Li Zhang; Weida Zhou; Guirong Chen; Hongjie Zhou; Ning Ye; Licheng Jiao

doi:10.1117/12.833908

30 October 2009 Pre-extracting boundary vectors for support vector machine using pseudo-density estimation method

Li Zhang, Weida Zhou, Guirong Chen, Hongjie Zhou, Ning Ye, Licheng Jiao

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74960J (2009) https://doi.org/10.1117/12.833908
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

Support vector machines (SVMs) have become useful and universal learning machines. SVMs construct a decision function by support vectors (SVs) and their corresponding weights. The training phase of SVMs definitely uses all training samples, which leads to a large computational complexity for a large scale sample set. Moreover support vectors could not be found until a quadratic programming (QP) problem is solved. Actually we know only SVs play a role in the decision function. Hence, pseudo density estimation (PDE) is presented to extract a set of boundary vectors (BVs) which may contain SVs. The PDE method is a variant of Parzen window method. Hyperspheres are considered as the window functions. In our method, for each sample we construct a hypersphere with an unfixed radius. The ratio of the number of samples contained in the hypersphere of a sample to the total training samples can be taken as the pseudo density of the corresponding sample. The set of BVs is taken as the training input to SVMs. In doing so, it speeds the training procedure of SVMs. It is convenient for PDE to determine its parameter. The experiments show that SVMs using PDE have the similar generalization performance to SVMs.

Citation Download Citation

Li Zhang, Weida Zhou, Guirong Chen, Hongjie Zhou, Ning Ye, and Licheng Jiao "Pre-extracting boundary vectors for support vector machine using pseudo-density estimation method", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74960J (30 October 2009); https://doi.org/10.1117/12.833908

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