Jäkel, F. ; Schölkopf, B. ; Wichmann, F. A. (2009)
Does Cognitive Science Need Kernels.
In: Trends in Cognitive Sciences, 13
doi: 10.1016/j.tics.2009.06.002
Artikel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2009 |
| Autor(en): | Jäkel, F. ; Schölkopf, B. ; Wichmann, F. A. |
| Art des Eintrags: | Bibliographie |
| Titel: | Does Cognitive Science Need Kernels |
| Sprache: | Englisch |
| Publikationsjahr: | 2009 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Trends in Cognitive Sciences |
| Jahrgang/Volume einer Zeitschrift: | 13 |
| DOI: | 10.1016/j.tics.2009.06.002 |
| URL / URN: | https://doi.org/10.1016/j.tics.2009.06.002 |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Psychologie 03 Fachbereich Humanwissenschaften > Institut für Psychologie > Modelle höherer Kognition |
| Hinterlegungsdatum: | 09 Jul 2018 09:16 |
| Letzte Änderung: | 12 Okt 2020 11:11 |
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