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  Support vector method for novelty detection

Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (2000). Support vector method for novelty detection. In S. Solla, T. Leen, & K. Müller (Eds.), Advances in Neural Information Processing Systems 12 (pp. 582-588). Cambridge, MA, USA: MIT Press.

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 Creators:
Schölkopf, B1, Author              
Williamson , RC, Author
Smola, AJ, Author              
Shawe-Taylor, J, Author
Platt, JC, Author
Affiliations:
1External Organizations, ou_persistent22              

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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: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 815
 Degree: -

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Title: Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999)
Place of Event: Denver, CO, USA
Start-/End Date: 2000-11-29 - 2000-12-04

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Title: Advances in Neural Information Processing Systems 12
Source Genre: Proceedings
 Creator(s):
Solla, SA, Editor
Leen, TK, Editor
Müller, K, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 582 - 588 Identifier: ISBN: 0-262-11245-0