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2006
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Clustering based large margin classification: a scalable approach using SOCP formulation

9 years 9 months ago
Clustering based large margin classification: a scalable approach using SOCP formulation
This paper presents a novel Second Order Cone Programming (SOCP) formulation for large scale binary classification tasks. Assuming that the class conditional densities are mixture distributions, where each component of the mixture has a spherical covariance, the second order statistics of the components can be estimated efficiently using clustering algorithms like BIRCH. For each cluster, the second order moments are used to derive a second order cone constraint via a Chebyshev-Cantelli inequality. This constraint ensures that any data point in the cluster is classified correctly with a high probability. This leads to a large margin SOCP formulation whose size depends on the number of clusters rather than the number of training data points. Hence, the proposed formulation scales well for large datasets when compared to the sate-of-the-art classifiers, Support Vector Machines (SVMs). Experiments on real world and synthetic datasets show that the proposed algorithm outperforms SVM solve...
J. Saketha Nath, Chiranjib Bhattacharyya, M. Naras
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2006
Where KDD
Authors J. Saketha Nath, Chiranjib Bhattacharyya, M. Narasimha Murty
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