Probabilistic Kernel Regression Models

Tommi S. Jaakkola, David Haussler
Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2, 1999.

Abstract

We introduce a class of flexible conditional probability models and techniques for classification/regression problems. Many existing methods such as generalized linear models and support vector machines are subsumed under this class. The flexibility of this class of techniques comes from the use of kernel functions as in support vector machines, and the generality from dual formulations of standard regression models.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR2-jaakkola99a, title = {Probabilistic kernel regression models}, author = {Jaakkola, Tommi S. and Haussler, David}, booktitle = {Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics}, year = {1999}, editor = {Heckerman, David and Whittaker, Joe}, volume = {R2}, series = {Proceedings of Machine Learning Research}, month = {03--06 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r2/jaakkola99a/jaakkola99a.pdf}, url = {https://proceedings.mlr.press/r2/jaakkola99a.html}, abstract = {We introduce a class of flexible conditional probability models and techniques for classification/regression problems. Many existing methods such as generalized linear models and support vector machines are subsumed under this class. The flexibility of this class of techniques comes from the use of kernel functions as in support vector machines, and the generality from dual formulations of standard regression models.}, note = {Reissued by PMLR on 20 August 2020.} }
Endnote
%0 Conference Paper %T Probabilistic Kernel Regression Models %A Tommi S. Jaakkola %A David Haussler %B Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1999 %E David Heckerman %E Joe Whittaker %F pmlr-vR2-jaakkola99a %I PMLR %U https://proceedings.mlr.press/r2/jaakkola99a.html %V R2 %X We introduce a class of flexible conditional probability models and techniques for classification/regression problems. Many existing methods such as generalized linear models and support vector machines are subsumed under this class. The flexibility of this class of techniques comes from the use of kernel functions as in support vector machines, and the generality from dual formulations of standard regression models. %Z Reissued by PMLR on 20 August 2020.
APA
Jaakkola, T.S. & Haussler, D.. (1999). Probabilistic Kernel Regression Models. Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R2 Available from https://proceedings.mlr.press/r2/jaakkola99a.html. Reissued by PMLR on 20 August 2020.

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