PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods
matthias seeger
In: Neural Information Processing Systems 2006, 4 Dec - 7 Dec 2006, Vancouver, CA.

Abstract

We propose a highly efficient framework for kernel multi-class models with a large and structured set of classes. Kernel parameters are learned automatically by maximizing the cross-validation log likelihood, and predictive probabilities are estimated. We demonstrate our approach on large scale text classification tasks with hierarchical class structure, achieving state-of-the-art results in an order of magnitude less time than previous work.

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EPrint Type:Conference or Workshop Item (Paper)
Additional Information:Available at http://www.kyb.tuebingen.mpg.de/bs/people/seeger/
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:2701
Deposited By:matthias seeger
Deposited On:19 December 2007