Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32244
Title: Sample reduction for SVMs via data structure analysis
Authors: Wang, DF
Yeung, DS
Tsang, ECC
Keywords: Data structures
Support vector machines
Issue Date: 2007
Publisher: IEEE
Source: IEEE International Conference on System of Systems Engineering, 2007 : SoSE '07, 16-18 April 2007, San Antonio, TX, p. 1-6 How to cite?
Abstract: This paper presents a new sample reduction algorithm, sample reduction by data structure analysis (SR-DSA), for SVMs to improve their scalability. SR-DSA utilizes data structure information in determining which data points are not useful in learning the separating plane and could be removed. As this algorithm is performed before SVMs training, it avoids the problem suffered by most sample reduction methods whose choices of samples heavily depend on repeatedly training of SVMs. Experiments on both synthetic and real world datasets have shown that SR-DSA is capable of reducing the number of samples as well as the time for SVMs training while maintaining high testing accuracy.
URI: http://hdl.handle.net/10397/32244
ISBN: 1-4244-1159-9
1-4244-1160-2 (E-ISBN)
DOI: 10.1109/SYSOSE.2007.4304333
Appears in Collections:Conference Paper

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

Page view(s)

18
Last Week
0
Last month
Checked on May 21, 2017

Google ScholarTM

Check

Altmetric



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