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  Classification on proximity data with LP-machines

Graepel, T., Herbrich, R., Schölkopf, B., Smola, A., Bartlett, P., Müller, K., et al. (1999). Classification on proximity data with LP-machines. In Ninth International Conference on Artificial Neural Networks ICANN 99 (pp. 304-309). London, UK: Institute of Electrical Engineers.

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Graepel, T, Author
Herbrich, R, Author
Schölkopf, B1, Author           
Smola, AJ, Author           
Bartlett, P, Author
Müller, K, Author
Obermayer, K, Author
Williamson, RC, Author
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1External Organizations, ou_persistent22              

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 Abstract: We provide a new linear program to deal with classification of data in the case of data given in terms of pairwise proximities. This allows to avoid the problems inherent in using feature spaces with indefinite metric in support vector machines, since the notion of a margin is purely needed in input space where the classification actually occurs. Moreover in our approach we can enforce sparsity in the proximity representation by sacrificing training error. This turns out to be favorable for proximity data. Similar to /spl nu/-SV methods, the only parameter needed in the algorithm is the (asymptotical) number of data points being classified with a margin. Finally, the algorithm is successfully compared with /spl nu/-SV learning in proximity space and K-nearest-neighbors on real world data from neuroscience and molecular biology.

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 Dates: 1999-09
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 811
DOI: 10.1049/cp:19991126
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Title: Ninth International Conference on Artificial Neural Networks (ICANN 1999)
Place of Event: Edinburgh, UK
Start-/End Date: 1999-09-07 - 1999-09-10

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Title: Ninth International Conference on Artificial Neural Networks ICANN 99
Source Genre: Proceedings
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Publ. Info: London, UK : Institute of Electrical Engineers
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 304 - 309 Identifier: ISBN: 0-85296-721-7

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Title: Conference Publication of the Institution of Electrical Engineers
Source Genre: Proceedings
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Pages: - Volume / Issue: 470 Sequence Number: - Start / End Page: - Identifier: -