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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Zhang, D., Zuo, W., Wang, P. (2018). Edit Distance for Pulse Diagnosis. In: Computational Pulse Signal Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-10-4044-3_11
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DOI: https://doi.org/10.1007/978-981-10-4044-3_11
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