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conference paper
Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods
2007
Proceedings of the 20th Annual Conference on Neural Information Processing Systems
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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nips06.pdf
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