Please use this identifier to cite or link to this item:
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?
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
ISBN: 9780769541099
ISSN: 1051-4651
DOI: 10.1109/ICPR.2010.670
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Feb 6, 2019

Page view(s)

Last Week
Last month
Citations as of Feb 17, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.