Kernel Dependency Estimation
Abstract
We consider the learning problem of finding a dependency between a general class of objects and another, possibly different, general class of objects. The objects can be for example: vectors, images, strings, trees or graphs. Such a task is made possible by employing similarity measures in both input and output spaces using ker(cid:173) nel functions, thus embedding the objects into vector spaces. We experimentally validate our approach on several tasks: mapping strings to strings, pattern recognition, and reconstruction from par(cid:173) tial images.
Cite
Text
Weston et al. "Kernel Dependency Estimation." Neural Information Processing Systems, 2002.Markdown
[Weston et al. "Kernel Dependency Estimation." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/weston2002neurips-kernel/)BibTeX
@inproceedings{weston2002neurips-kernel,
title = {{Kernel Dependency Estimation}},
author = {Weston, Jason and Chapelle, Olivier and Vapnik, Vladimir and Elisseeff, André and Schölkopf, Bernhard},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {897-904},
url = {https://mlanthology.org/neurips/2002/weston2002neurips-kernel/}
}