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Training support vector machines: an application to well-log data classification

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3 Author(s)
Yan Hui ; Dept. of Autom., Tsinghua Univ., Beijing, China ; Zhang Xuegong ; Zhang Xianda

In this paper, we investigate the application of support vector machines (SVM) in pattern recognition. SVM is a learning technique developed by Vapnik et al. (1997) that can be seen as a new method for training polynomial, neural network, or radial basis functions classifiers. The decision surfaces are found by solving a linearly constrained quadratic programming problem. We present experimental results of our implementation of SVM, and demonstrate its advantage on well-log data classification problem

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Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on  (Volume:3 )

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