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A Novel Support Vector Machine with Globality-Locality Preserving
Joint Authors
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-17
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Support vector machine (SVM) is regarded as a powerful method for pattern classification.
However, the solution of the primal optimal model of SVM is susceptible for class distribution and may result in a nonrobust solution.
In order to overcome this shortcoming, an improved model, support vector machine with globality-locality preserving (GLPSVM), is proposed.
It introduces globality-locality preserving into the standard SVM, which can preserve the manifold structure of the data space.
We complete rich experiments on the UCI machine learning data sets.
The results validate the effectiveness of the proposed model, especially on the Wine and Iris databases; the recognition rate is above 97% and outperforms all the algorithms that were developed from SVM.
American Psychological Association (APA)
Ma, Cheng-Long& Yuan, Y.-B.. 2014. A Novel Support Vector Machine with Globality-Locality Preserving. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1051430
Modern Language Association (MLA)
Ma, Cheng-Long& Yuan, Y.-B.. A Novel Support Vector Machine with Globality-Locality Preserving. The Scientific World Journal No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-1051430
American Medical Association (AMA)
Ma, Cheng-Long& Yuan, Y.-B.. A Novel Support Vector Machine with Globality-Locality Preserving. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1051430
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051430