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- T. Graepel, R. Herbrich,
B. Schölkopf, A. Smola, P. Bartlett, K. R. Müller, K. Obermayer, and
R. Williamson. Classification on Proximity Data with LP-Machines.
.
In 9th International Conference on Artificial Neural Networks -
ICANN99, pages 304-309, 1999.
(FTP Gzipped PostScript, 6 pages, 42 kb)
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
ininput 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 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 nu --SV
learning in proximity space and K--nearest-neighbors on real world data
from Neuroscience and molecular biology.
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