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Proceedings Paper

Efficient sequence classification by R2-Kernel
Author(s): Hansheng Lei
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Paper Abstract

A novel kernel, named R2-kernel, is presented for efficient sequence classification based on the Support Vector Machine (SVM). As an intuitive similarity measure, R2 naturally introduces a stop technique in multi-class SVM evaluation, that is, when there exist K support vectors from class i that has similarity beyond a certain threshold with testing sequence X, we can assign sequence X to class i directly and stop the SVM evaluation. The stop technique is seamless integrated into multi-class SVM to improve its evaluation efficiency without negative impact on the performance. Experimental results confirmed the efficiency of the stop technique introduced by the R2-kernel.

Paper Details

Date Published: 28 January 2008
PDF: 5 pages
Proc. SPIE 6809, Visualization and Data Analysis 2008, 68090L (28 January 2008); doi: 10.1117/12.761830
Show Author Affiliations
Hansheng Lei, Univ. of Texas at Brownsville (United States)

Published in SPIE Proceedings Vol. 6809:
Visualization and Data Analysis 2008
Katy Börner; Matti T. Gröhn; Jinah Park; Jonathan C. Roberts, Editor(s)

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