Probabilistic Kernel Regression Models

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.

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Text

Jaakkola and Haussler. "Probabilistic Kernel Regression Models." Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999.

Markdown

[Jaakkola and Haussler. "Probabilistic Kernel Regression Models." Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999.](https://mlanthology.org/aistats/1999/jaakkola1999aistats-probabilistic/)

BibTeX

@inproceedings{jaakkola1999aistats-probabilistic,
  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},
  volume    = {R2},
  url       = {https://mlanthology.org/aistats/1999/jaakkola1999aistats-probabilistic/}
}