Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13218
Title: Gaussian ERP kernel classifier for pulse waveforms classification
Authors: Zhang, D
Zuo, W
Zhang, D 
Li, Y
Li, N
Keywords: Edit distance with real penalty
K-nearest neighbors
Kernel method
Pulse diagnosis
Pulse waveform
Issue Date: 2010
Source: Proceedings - International Conference on Pattern Recognition, 2010, p. 2736-2739 How to cite?
Journal: Proceedings - International Conference on Pattern Recognition 
Abstract: While advances in sensor and signal processing techniques have provided effective tools for quantitative research on traditional Chinese pulse diagnosis (TCPD), the automatic classification of pulse waveforms is remained a difficult problem. To address this issue, this paper proposed a novel edit distance with real penalty (ERP)-based k-nearest neighbors (KNN) classifier by referring to recent progresses in time series matching and KNN classifier. Taking advantage of the metric property of ERP, we first develop a Gaussian ERP kernel, and then embed it into kernel difference-weighted KNN classifier. The proposed Gaussian ERP kernel classifier is evaluated on a dataset which includes 2470 pulse waveforms. Experimental results show that the proposed classifier is much more accurate than several other pulse waveform classification approaches.
Description: 2010 20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, 23-26 August 2010
URI: http://hdl.handle.net/10397/13218
ISBN: 9780769541099
ISSN: 1051-4651
DOI: 10.1109/ICPR.2010.670
Appears in Collections:Conference Paper

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