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New Support Vector Algorithms




We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter {nu} lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter {epsilon} in the regression case, and the regularization constant C in the classification case. We describe the algorithms, give some theoretical results concerning the meaning and the choice of {nu}, and report experimental results.

Author(s): Schölkopf, B. and Smola, AJ. and Williamson, RC. and Bartlett, PL.
Journal: Neural Computation
Volume: 12
Number (issue): 5
Pages: 1207-1245
Year: 2000
Month: May
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: doi:10.1162/089976600300015565
Institution: Royal Holloway College
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {New Support Vector Algorithms},
  author = {Sch{\"o}lkopf, B. and Smola, AJ. and Williamson, RC. and Bartlett, PL.},
  journal = {Neural Computation},
  volume = {12},
  number = {5},
  pages = {1207-1245},
  organization = {Max-Planck-Gesellschaft},
  institution = {Royal Holloway College},
  school = {Biologische Kybernetik},
  month = may,
  year = {2000},
  month_numeric = {5}