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  Cross-validation Optimization for Large Scale Structured Classification Kernel Methods

Seeger, M. (2008). Cross-validation Optimization for Large Scale Structured Classification Kernel Methods. The Journal of Machine Learning Research, 9, 1147-1178.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C8C5-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-3042-C
Genre: Journal Article

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 Creators:
Seeger, M1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We propose a highly efficient framework for penalized likelihood kernel methods applied to multi-class models with a large, structured set of classes. As opposed to many previous approaches which try to decompose the fitting problem into many smaller ones, we focus on a Newton optimization of the complete model, making use of model structure and linear conjugate gradients in order to approximate Newton search directions. Crucially, our learning method is based entirely on matrix-vector multiplication primitives with the kernel matrices and their derivatives, allowing straightforward specialization to new kernels, and focusing code optimization efforts to these primitives only. Kernel parameters are learned automatically, by maximizing the cross-validation log likelihood in a gradient-based way, and predictive probabilities are estimated. We demonstrate our approach on large scale text classification tasks with hierarchical structure on thousands of classes, achieving state-of-the-art results in an order of magnitude less time than previous work.

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 Dates: 2008-06
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 5242
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Title: The Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 9 Sequence Number: - Start / End Page: 1147 - 1178 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020_1