Cross-Validation Optimization for Large Scale Hierarchical Classification 
Kernel Methods

Seeger, M

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Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods

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

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https://papers.nips.cc/paper/3044-cross-validation-optimization-for-large-scale-hierarchical-classification-kernel-methods.pdf
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Citation

Seeger, M. (2007). Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods. In B. Schölkopf, J. Platt, & T. Hoffman (Eds.), Advances in Neural Information Processing Systems 19 (pp. 1233-1240). Cambridge, MA, USA: MIT Press.

Cite as: https://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.