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  Kernel Dependency Estimation

Weston, J., Chapelle, O., Elisseeff, A., Schölkopf, B., & Vapnik, V.(2002). Kernel Dependency Estimation (98). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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MPIK-TR-98.pdf (Publisher version), 264KB
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 Creators:
Weston, J1, 2, Author           
Chapelle, O1, 2, Author           
Elisseeff, A1, 2, Author           
Schölkopf, B1, 2, Author           
Vapnik, V, Author           
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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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 kernel functions, thus embedding the objects into vector spaces. Output kernels also make it possible to encode prior information and/or invariances in the loss function in an elegant way. We experimentally validate our approach on several tasks: mapping strings to strings, pattern recognition, and reconstruction from partial images.

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 Dates: 2002-08
 Publication Status: Published in print
 Pages: 10
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
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 Identifiers: Report Nr.: 98
BibTex Citekey: 1864
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
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Pages: - Volume / Issue: 98 Sequence Number: - Start / End Page: - Identifier: -