PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Does Cognitive Science Need Kernels?
F. Jäkel, B. Schölkopf and F.A. Wichmann
Trends in Cognitive Sciences Volume 13, Number 9, pp. 381-388, 2009.


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 about their behavior are therefore potentially relevant for human category learning. In particular, we think that kernel methods show the prospect of providing explanations ranging from the implementational via the algorithmic to the computational level.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Brain Computer Interfaces
Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:6303
Deposited By:Bernhard Schölkopf
Deposited On:08 March 2010