Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods

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.


Published in:
Proceedings of the 20th Annual Conference on Neural Information Processing Systems, 1233-1240
Presented at:
Neural Information Processing Systems 19, Vancouver, BC
Year:
2007
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 Record created 2010-12-01, last modified 2018-03-17

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