date: 2016-02-22T13:06:20Z
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pdf:docinfo:title: Learning From Small Samples: An Analysis of Simple Decision Heuristics
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subject: Neural Information Processing Systems http://nips.cc/
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dc:title: Learning From Small Samples: An Analysis of Simple Decision Heuristics
Book: Advances in Neural Information Processing Systems 28
pdf:docinfo:custom:Date: 2015
modified: 2016-02-22T13:06:20Z
Description-Abstract: Simple decision heuristics are models of human and animal behavior that use few pieces of information---perhaps only a single piece of information---and integrate the pieces in simple ways, for example, by considering them sequentially, one at a time, or by giving them equal weight. It is unknown how quickly these heuristics can be learned from experience. We show, analytically and empirically, that only a few training samples lead to substantial progress in learning. We focus on three families of heuristics: single-cue decision making, lexicographic decision making, and tallying. Our empirical analysis is the most extensive to date, employing 63 natural data sets on diverse subjects.
cp:subject: Neural Information Processing Systems http://nips.cc/
pdf:docinfo:subject: Neural Information Processing Systems http://nips.cc/
pdf:docinfo:custom:Created: 2015
pdf:docinfo:creator: Özgür ?im?ek, Marcus Buckmann
meta:author: Özgür ?im?ek, Marcus Buckmann
meta:creation-date: 2016-02-22T13:06:20Z
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lastpage: 3149
pdf:docinfo:custom:Type: Conference Proceedings
Editors: C. Cortes and N.D. Lawrence and D.D. Lee and M. Sugiyama and R. Garnett
Author: Özgür ?im?ek, Marcus Buckmann
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pdf:docinfo:custom:Description: Paper accepted and presented at the Neural Information Processing Systems Conference (http://nips.cc/)
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dc:description: Neural Information Processing Systems http://nips.cc/
Description: Paper accepted and presented at the Neural Information Processing Systems Conference (http://nips.cc/)
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firstpage: 3141
dc:creator: Özgür ?im?ek, Marcus Buckmann
description: Neural Information Processing Systems http://nips.cc/
pdf:docinfo:custom:EventType: Poster
dcterms:created: 2016-02-22T13:06:20Z
Last-Modified: 2016-02-22T13:06:20Z
dcterms:modified: 2016-02-22T13:06:20Z
title: Learning From Small Samples: An Analysis of Simple Decision Heuristics
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Created: 2015
pdf:docinfo:modified: 2016-02-22T13:06:20Z
pdf:docinfo:custom:Book: Advances in Neural Information Processing Systems 28
Language: en-US
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creator: Özgür ?im?ek, Marcus Buckmann
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pdf:docinfo:custom:Editors: C. Cortes and N.D. Lawrence and D.D. Lee and M. Sugiyama and R. Garnett
pdf:docinfo:custom:Description-Abstract: Simple decision heuristics are models of human and animal behavior that use few pieces of information---perhaps only a single piece of information---and integrate the pieces in simple ways, for example, by considering them sequentially, one at a time, or by giving them equal weight. It is unknown how quickly these heuristics can be learned from experience. We show, analytically and empirically, that only a few training samples lead to substantial progress in learning. We focus on three families of heuristics: single-cue decision making, lexicographic decision making, and tallying. Our empirical analysis is the most extensive to date, employing 63 natural data sets on diverse subjects.
pdf:docinfo:custom:Published: 2015
Published: 2015
pdf:docinfo:custom:Publisher: Curran Associates, Inc.
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pdf:docinfo:created: 2016-02-22T13:06:20Z