Date of Original Version

6-2011

Type

Conference Proceeding

Rights Management

Copyright 2011 by the author(s)/owner(s)

Abstract or Description

We present Infinite SVM (iSVM), a Dirichlet process mixture of large-margin kernel machines for multi-way classification. An iSVM enjoys the advantages of both Bayesian nonparametrics in handling the unknown number of mixing components, and large-margin kernel machines in robustly capturing local nonlinearity of complex data. We develop an efficient variational learning algorithm for posterior inference of iSVM, and we demonstrate the advantages of iSVM over Dirichlet process mixture of generalized linear models and other benchmarks on both synthetic and real Flickr image classification datasets.

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Published In

Proceedings of the 28th International Conference on Machine Learning.