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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
Published in:
Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on
(Volume:3
)
Date of Conference: 2000
- Page(s):
- 1427 - 1431 vol.3
- Meeting Date :
- 21 Aug 2000-25 Aug 2000
- Print ISBN:
- 0-7803-5747-7
- INSPEC Accession Number:
- 6939293
- Conference Location :
- Beijing
- Digital Object Identifier :
- 10.1109/ICOSP.2000.893369
- Product Type:
- Conference Publications
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