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Published Articles >> Table of Contents >> Abstract
The 2nd Canadian Conference on Computer and Robot Vision (CRV'05)
pp. 246-252
Auto-Correlation Wavelet Support Vector Machine and Its Applications to Regression
Guangyi Chen, McGill University, Canada
Gregory Dudek, McGill University, Canada
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CRV.2005.19
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| Abstract |
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A Support Vector Machine (SVM) with the auto-correlation of compactly supported wavelet as kernel is proposed in this paper. It is proved that this kernel is an admissible support vector kernel. The main advantage of the auto-correlation of a compactly supported wavelet is that it satisfies the translation invariant property, which is very important for signal processing. Also, we can choose a better wavelet from different choices of wavelet families for our auto-correlation wavelet kernel. Experiments on signal regression show that this method is better than the existing SVM function regression with the scalar wavelet kernel, the Gaussian kernel, and the exponential radial basis function kernel. It can be easily extended to other applications such as pattern recognition by using this newly developed auto-correlation wavelet SVM.
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 Additional Information
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Index Terms- Wavelets, support vector machine, machine learning, function regression
Citation:
Guangyi Chen, Gregory Dudek,
"Auto-Correlation Wavelet Support Vector Machine and Its Applications to Regression,"
crv,
pp. 246-252,
The 2nd Canadian Conference on Computer and Robot Vision (CRV'05),
2005
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