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  Building Support Vector Machines with Reduced Classifier Complexity

Keerthi, S., Chapelle, O., & DeCoste, D. (2006). Building Support Vector Machines with Reduced Classifier Complexity. The Journal of Machine Learning Research, 7, 1493-1515.

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Keerthi, S, Author
Chapelle, O1, 2, Author           
DeCoste, D, 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: Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overcome this problem we devise a primal method with the following properties: (1) it decouples the idea of basis functions from the concept of support vectors; (2) it greedily finds a set of kernel basis functions of a specified maximum size (dmax) to approximate the SVM primal cost function well; (3) it is efficient and roughly scales as O(ndmax^2) where n is the number of training examples; and, (4) the number of basis functions it requires to achieve an accuracy close to the SVM accuracy is usually far less than the number of SVM support vectors.

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 Dates: 2006-07
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: BibTex Citekey: 3598
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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: 7 Sequence Number: - Start / End Page: 1493 - 1515 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020_1