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Efficient Additive Kernels via Explicit Feature Maps

Abstract:

Large scale nonlinear support vector machines (SVMs) can be approximated by linear ones using a suitable feature map. The linear SVMs are in general much faster to learn and evaluate (test) than the original nonlinear SVMs. This work introduces explicit feature maps for the additive class of kernels, such as the intersection, Hellinger's, and χ 2 kernels, commonly used in computer vision, and enables their use in large scale problems. In particular, we: 1) provide explicit feature maps for al...

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Publication status:
Published

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Publisher copy:
10.1109/TPAMI.2011.153

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
Journal:
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE More from this journal
Volume:
34
Issue:
3
Pages:
480-492
Publication date:
2012-03-01
DOI:
EISSN:
2160-9292
ISSN:
0162-8828
Language:
English
Keywords:
Pubs id:
pubs:314207
UUID:
uuid:a8659b1d-f15e-42ee-a928-c9042e753e48
Local pid:
pubs:314207
Source identifiers:
314207
Deposit date:
2012-12-19

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