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Transductive Inference for Estimating Values of Functions

2000

Conference Paper

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We introduce an algorithm for estimating the values of a function at a set of test points $x_1^*,dots,x^*_m$ given a set of training points $(x_1,y_1),dots,(x_ell,y_ell)$ without estimating (as an intermediate step) the regression function. We demonstrate that this direct (transductive) way for estimating values of the regression (or classification in pattern recognition) is more accurate than the traditional one based on two steps, first estimating the function and then calculating the values of this function at the points of interest.

Author(s): Chapelle, O. and Vapnik, V. and Weston, J.
Book Title: Advances in Neural Information Processing Systems 12
Journal: Advances in Neural Information Processing Systems
Pages: 421-427
Year: 2000
Month: June
Day: 0
Editors: Solla, S.A. , T.K. Leen, K-R M{\"u}ller
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999)
Event Place: Denver, CO, USA

Address: Cambridge, MA, USA
Digital: 0
ISBN: 0-262-19450-3
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{2162,
  title = {Transductive Inference for Estimating Values of Functions},
  author = {Chapelle, O. and Vapnik, V. and Weston, J.},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 12},
  pages = {421-427},
  editors = {Solla, S.A. , T.K. Leen, K-R M{\"u}ller},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = jun,
  year = {2000},
  month_numeric = {6}
}