Sample reduction based on kernel squared Mahalanobis distance for support vector machines

Sample reduction based on kernel squared Mahalanobis distance for support vector machines

This paper presents a sample reduction algorithm based on kernel squared Mahalanobis distance, as a sampling preprocessing for SVM training to improve the scalability. Experimental results show that, the proposed algorithm is effective for reducing training samples for nonlinear SVMs.

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Bibliographische Detailangaben
Hauptverfasser: Xiao-Lin Zou, Xiao-Zhang Liu
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Computer applications
Covariance matrix
Kernel
Modeling
Support vector machines
Symmetric matrices
Training
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Beschreibung
Zusammenfassung:This paper presents a sample reduction algorithm based on kernel squared Mahalanobis distance, as a sampling preprocessing for SVM training to improve the scalability. Experimental results show that, the proposed algorithm is effective for reducing training samples for nonlinear SVMs.
ISSN:2161-9069
DOI:10.1109/ICCASM.2010.5623208