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Published in:
Introduction: Computational Pulse Diagnosis Read chapter Read first chapter

2018 | OriginalPaper | Chapter

11. Edit Distance for Pulse Diagnosis

Authors : David Zhang, Wangmeng Zuo, Peng Wang

Published in: Computational Pulse Signal Analysis

Publisher: Springer Singapore

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Abstract

In this chapter, by referring to the edit distance with real penalty (ERP) and the recent progress in k-nearest neighbors (KNN) classifiers, we propose two novel ERP-based KNN classifiers. Taking advantage of the metric property of ERP, we first develop an ERP-induced inner product and a Gaussian ERP kernel, then embed them into difference-weighted KNN classifiers, and finally develop two novel classifiers for pulse waveform classification. The experimental results show that the proposed classifiers are effective for accurate classification of pulse waveform.

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previous chapter Characterization of Inter-Cycle Variations for Wrist Pulse Diagnosis
next chapter Modified Gaussian Models and Fuzzy C-Means
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Metadata
Title
Edit Distance for Pulse Diagnosis
Authors
David Zhang
Wangmeng Zuo
Peng Wang
Copyright Year
2018
Publisher
Springer Singapore
Book
Computational Pulse Signal Analysis

Print ISBN: 978-981-10-4043-6

Electronic ISBN: 978-981-10-4044-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-4044-3_11

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