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  Transductive Inference for Estimating Values of Functions

Chapelle, O., Vapnik, V., & Weston, J. (2000). Transductive Inference for Estimating Values of Functions. Advances in Neural Information Processing Systems, 421-427.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E4CE-8 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E4CF-6
Genre: Conference Paper

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 Creators:
Chapelle, O1, Author           
Vapnik, V, Author
Weston, J1, Author           
Solla, Editor
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: 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.

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 Dates: 2000-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-19450-3
URI: http://books.nips.cc/nips12.html
BibTex Citekey: 2162
 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: 421 - 427 Identifier: -