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  Does Cognitive Science Need Kernels?

Jäkel, F., Schölkopf, B., & Wichmann, F. (2009). Does Cognitive Science Need Kernels? Trends in Cognitive Sciences, 13(9), 381-388. doi:10.1016/j.tics.2009.06.002.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C2E0-E Version Permalink: http://hdl.handle.net/21.11116/0000-0002-BE23-1
Genre: Journal Article

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 Creators:
Jäkel, F, Author              
Schölkopf, B1, 2, Author              
Wichmann, FA, 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: Kernel methods are among the most successful tools in machine learning and are used in challenging data analysis problems in many disciplines. Here we provide examples where kernel methods have proven to be powerful tools for analyzing behavioral data, especially for identifying features in categorization experiments. We also demonstrate that kernel methods relate to perceptrons and exemplar models of categorization. Hence, we argue that kernel methods have neural and psychological plausibility, and theoretical results concerning their behavior are therefore potentially relevant for human category learning. In particular, we believe kernel methods have the potential to provide explanations ranging from the implementational via the algorithmic to the computational level.

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 Dates: 2009-09
 Publication Status: Published in print
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1016/j.tics.2009.06.002
BibTex Citekey: 6061
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Title: Trends in Cognitive Sciences
  Other : Trends Cogn. Sci.
Source Genre: Journal
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Publ. Info: Kidlington, Oxford, UK : Elsevier Current Trends
Pages: - Volume / Issue: 13 (9) Sequence Number: - Start / End Page: 381 - 388 Identifier: ISSN: 1364-6613
CoNE: https://pure.mpg.de/cone/journals/resource/954925620155