Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9944
Title: Time series classification using support vector machine with Gaussian elastic metric kernel
Authors: Zhang, D
Zuo, W
Zhang, D 
Zhang, H
Keywords: Dynamic time warping
Kernel method
Support vector machine
Time series
Issue Date: 2010
Source: Proceedings - International Conference on Pattern Recognition, 2010, p. 29-32 How to cite?
Abstract: Motivated by the great success of dynamic time warping (DTW) in time series matching, Gaussian DTW kernel had been developed for support vector machine (SVM)-based time series classification. Counter-examples, however, had been subsequently reported that Gaussian DTW kernel usually cannot outperform Gaussian RBF kernel in the SVM framework. In this paper, by extending the Gaussian RBF kernel, we propose one novel class of Gaussian elastic metric kernel (GEMK), and present two examples of GEMK: Gaussian time warp edit distance (GTWED) kernel and Gaussian edit distance with real penalty (GERP) kernel. Experimental results on UCR time series data sets show that, in terms of classification accuracy, SVM with GEMK is much superior to SVM with Gaussian RBF kernel and Gaussian DTW kernel, and the state-of-the-art similarity measure methods.
Description: 2010 20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, 23-26 August 2010
URI: http://hdl.handle.net/10397/9944
ISBN: 9780769541099
ISSN: 1051-4651
DOI: 10.1109/ICPR.2010.16
Appears in Collections:Conference Paper

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