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Kernel Dependency Estimation


Technical Report


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.

Author(s): Weston, J. and Chapelle, O. and Elisseeff, A. and Schölkopf, B. and Vapnik, V.
Number (issue): 98
Year: 2002
Month: August
Day: 0

Department(s): Empirical Inference
Bibtex Type: Technical Report (techreport)

Institution: Max Planck Institute for Biological Cybernetics

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Kernel Dependency Estimation},
  author = {Weston, J. and Chapelle, O. and Elisseeff, A. and Sch{\"o}lkopf, B. and Vapnik, V.},
  number = {98},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics},
  school = {Biologische Kybernetik},
  month = aug,
  year = {2002},
  month_numeric = {8}