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Classification on proximity data with lp–machines
, 1999
"... We provide a new linear program to deal with classification of data in the case of functions written 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 i ..."
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Cited by 47 (10 self)
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We provide a new linear program to deal with classification of data in the case of functions written 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 –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 –SV learning in proximity space and K–nearestneighbors on real world data from Neuroscience and molecular biology. 1
Regularized Principal Manifolds
 In Computational Learning Theory: 4th European Conference
, 2001
"... Many settings of unsupervised learning can be viewed as quantization problems  the minimization ..."
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Cited by 46 (5 self)
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Many settings of unsupervised learning can be viewed as quantization problems  the minimization
unknown title
, 2007
"... www.elsevier.com/locate/dam Estimates of covering numbers of convex sets with slowly decaying orthogonal subsets ..."
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www.elsevier.com/locate/dam Estimates of covering numbers of convex sets with slowly decaying orthogonal subsets
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"... This article was published in an Elsevier journal. The attached copy is furnished to the author for noncommercial research and education use, including for instruction at the author’s institution, sharing with colleagues and providing to institution administration. Other uses, including reproductio ..."
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This article was published in an Elsevier journal. The attached copy is furnished to the author for noncommercial research and education use, including for instruction at the author’s institution, sharing with colleagues and providing to institution administration. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit: