TY - GEN
T1 - Time series classification using support vector machine with Gaussian elastic metric kernel
AU - Zhang, Dongyu
AU - Zuo, Wangmeng
AU - Zhang, David
AU - Zhang, Hongzhi
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Dynamic time warping
KW - Kernel method
KW - Support vector machine
KW - Time series
UR - https://www.scopus.com/pages/publications/78149485597
U2 - 10.1109/ICPR.2010.16
DO - 10.1109/ICPR.2010.16
M3 - 会议稿件
AN - SCOPUS:78149485597
SN - 9780769541099
T3 - Proceedings - International Conference on Pattern Recognition
SP - 29
EP - 32
BT - Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
PB - Institute of Electrical and Electronics Engineers Inc.
ER -