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An Analysis of Inference with the Universum

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84226

Sinz,  FH
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83855

Chapelle,  O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83774

Agarwal,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Sinz, F., Chapelle, O., Agarwal, A., & Schölkopf, B. (2008). An Analysis of Inference with the Universum. Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007, 1369-1376.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C727-4
Zusammenfassung
We study a pattern classification algorithm which has recently been proposed by Vapnik and coworkers. It builds on a new inductive principle which assumes that in addition to positive and negative data, a third class of data is available, termed the Universum. We assay the behavior of the algorithm by establishing links with Fisher discriminant analysis and oriented PCA, as well as with an SVM in a projected subspace (or, equivalently, with a data-dependent reduced kernel). We also provide experimental results.