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Conference Paper

Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84205

Seeger,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Seeger, M. (2007). Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods. Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, 1233-1240.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CBDD-C
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.