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  Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods

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.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CBDD-C Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-CBDE-A
Genre: Conference Paper

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 Creators:
Seeger, M1, Author           
Schölkopf, Editor
B., Editor
Platt, J., Editor
Hofmann, T., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 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.

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 Dates: 2007-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-19568-2
URI: http://nips.cc/Conferences/2006/
BibTex Citekey: 4168
 Degree: -

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Title: Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006)
Place of Event: Vancouver, BC, Canada
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Title: Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference
Source Genre: Journal
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Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1233 - 1240 Identifier: -