Kernel Dependency Estimation

Weston, J; Chapelle, O; Elisseeff, A; Schölkopf, B; Vapnik, V

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Kernel Dependency Estimation

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Weston, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Chapelle, O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Elisseeff, A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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MPIK-TR-98.pdf
(出版社版), 264KB

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引用

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

引用: https://hdl.handle.net/11858/00-001M-0000-0013-DF3E-6

要旨

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.