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  Kernels, Associated Structures and Generalizations

Hein, M., & Bousquet, O.(2004). Kernels, Associated Structures and Generalizations (127). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D8B3-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-8B2C-1
Genre: Report

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MPIK-TR-127.pdf (Publisher version), 177KB
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 Creators:
Hein, M1, 2, Author              
Bousquet, O1, 2, Author              
Affiliations:
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: This paper gives a survey of results in the mathematical literature on positive definite kernels and their associated structures. We concentrate on properties which seem potentially relevant for Machine Learning and try to clarify some results that have been misused in the literature. Moreover we consider different lines of generalizations of positive definite kernels. Namely we deal with operator-valued kernels and present the general framework of Hilbertian subspaces of Schwartz which we use to introduce kernels which are distributions. Finally indefinite kernels and their associated reproducing kernel spaces are considered.

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 Dates: 2004-07
 Publication Status: Published in print
 Pages: 14
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 127
BibTex Citekey: 2816
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
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Pages: - Volume / Issue: 127 Sequence Number: - Start / End Page: - Identifier: -