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  Support vector method for novelty detection

Schölkopf, B., Williamson RC, Smola AJ, Shawe-Taylor, J., & Platt, J. (2000). Support vector method for novelty detection. Advances in Neural Information Processing Systems, 582-588.

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 Creators:
Schölkopf, B1, Author           
Williamson RC, Smola AJ, Shawe-Taylor, J, Author
Platt, JC, Author
Solla, S.A., Editor
Leen, T.K., Editor
Müller, K.-R., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: Suppose you are given some dataset drawn from an underlying probability distribution ¤ and you want to estimate a “simple” subset ¥ of input space such that the probability that a test point drawn from ¤ lies outside of ¥ equals some a priori specified ¦ between § and ¨. We propose a method to approach this problem by trying to estimate a function © which is positive on ¥ and negative on the complement. The functional form of © is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. We provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabelled data.

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 Dates: 2000-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-11245-0
URI: http://books.nips.cc/nips12.html
BibTex Citekey: 815
 Degree: -

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Title: Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999)
Place of Event: Denver, CO, USA
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Title: Advances in Neural Information Processing Systems
Source Genre: Journal
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Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 582 - 588 Identifier: -