Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

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Matthias Seeger


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.