Header logo is

Transductive Gaussian Process Regression with Automatic Model Selection

2006

Conference Paper

ei


n contrast to the standard inductive inference setting of predictive machine learning, in real world learning problems often the test instances are already available at training time. Transductive inference tries to improve the predictive accuracy of learning algorithms by making use of the information contained in these test instances. Although this description of transductive inference applies to predictive learning problems in general, most transductive approaches consider the case of classification only. In this paper we introduce a transductive variant of Gaussian process regression with automatic model selection, based on approximate moment matching between training and test data. Empirical results show the feasibility and competitiveness of this approach.

Author(s): Le, QV. and Smola, AJ. and Gärtner, T. and Altun, Y.
Journal: Machine Learning: ECML 2006
Pages: 306-317
Year: 2006
Month: September
Day: 0
Editors: F{\"u}rnkranz, J. , T. Scheffer, M. Spiliopoulou
Publisher: Springer

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

DOI: 10.1007/11871842_31
Event Name: 17th European Conference on Machine Learning (ECML 2006)
Event Place: Berlin, Germany

Address: Berlin, Germany
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web

BibTex

@inproceedings{5705,
  title = {Transductive Gaussian Process Regression with Automatic Model Selection},
  author = {Le, QV. and Smola, AJ. and G{\"a}rtner, T. and Altun, Y.},
  journal = {Machine Learning: ECML 2006},
  pages = {306-317},
  editors = {F{\"u}rnkranz, J. , T. Scheffer, M. Spiliopoulou},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = sep,
  year = {2006},
  month_numeric = {9}
}