Asharaf, S and Shevade, SK and Murty, Narasimha M (2006) Scalable non-linear Support Vector Machine using hierarchical clustering. In: 18th International Conference on Pattern Recognition (ICPR 2006), AUG 20-24, 2006, Hong Kong, pp. 908-911.
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This paper discusses a method for scaling SVM with Gaussian kernel function to handle large data sets by using a selective sampling strategy for the training set. It employs a scalable hierarchical clustering algorithm to construct cluster indexing structures of the training data in the kernel induced feature space. These are then used for selective sampling of the training data for SVM to impart scalability to the training process. Empirical studies made on real world data sets show that the proposed strategy performs well on large data sets.
|Item Type:||Conference Paper|
|Additional Information:||Copyright 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||26 Aug 2010 04:39|
|Last Modified:||10 Jun 2011 05:53|
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