Classification on proximity data with LP-machines

Thore Graepel, Ralf Herbrich, Bernhard Schoelkopf, Alex Smola, Peter Bartlett, Klaus Muller, Klaus Obermayer, Robert Williamson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

53 Citations (Scopus)

Abstract

We provide a new linear program to deal with classification of data in the case of data given in terms of pair-wise 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 ν-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 ν-SV learning in proximity space and K-nearest-neighbors on real world data from Neuroscience and molecular biology.

Original languageEnglish
Title of host publicationIEE Conference Publication
Pages304-309
Number of pages6
Volume1
Edition470
Publication statusPublished - 1999 Dec 1
Externally publishedYes
EventProceedings of the 1999 the 9th International Conference on 'Artificial Neural Networks (ICANN99)' - Edinburgh, UK
Duration: 1999 Sep 71999 Sep 10

Other

OtherProceedings of the 1999 the 9th International Conference on 'Artificial Neural Networks (ICANN99)'
CityEdinburgh, UK
Period99/9/799/9/10

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Graepel, T., Herbrich, R., Schoelkopf, B., Smola, A., Bartlett, P., Muller, K., Obermayer, K., & Williamson, R. (1999). Classification on proximity data with LP-machines. In IEE Conference Publication (470 ed., Vol. 1, pp. 304-309)