A Loser Can Be a Winner: Comparison of Two Instance-based Learning Models in a Market Entry Competition

Games ◽

10.3390/g2010136 ◽

2011 ◽

Vol 2 (1) ◽

pp. 136-162 ◽

Cited By ~ 9

Author(s):

Cleotilde Gonzalez ◽

Varun Dutt ◽

Tomás Lejarraga

Keyword(s):

Market Entry ◽

Learning Models ◽

Instance Based Learning

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Instance-based Learning Models of Training

PsycEXTRA Dataset ◽

10.1037/e578882012-021 ◽

2010 ◽

Author(s):

Cleotilde Gonzalez ◽

Varun Dutt

Keyword(s):

Learning Models ◽

Instance Based Learning

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On the nature of phonological cues in the acquisition of French gender categories: Evidence from instance-based learning models

Lingua ◽

10.1016/j.lingua.2009.06.007 ◽

2010 ◽

Vol 120 (4) ◽

pp. 879-900 ◽

Cited By ~ 9

Author(s):

Clive Matthews

Keyword(s):

Learning Models ◽

Instance Based Learning ◽

Gender Categories ◽

French Gender

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Instance-based Learning Models of Training

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193121005402721 ◽

2010 ◽

Vol 54 (27) ◽

pp. 2319-2323 ◽

Cited By ~ 5

Author(s):

Cleotilde Gonzalez ◽

Varun Dutt

Keyword(s):

Learning Models ◽

Instance Based Learning

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Supplemental Material for Reconciling Reinforcement Learning Models With Behavioral Extinction and Renewal: Implications for Addiction, Relapse, and Problem Gambling

Psychological Review ◽

10.1037/0033-295x.114.3.784.supp ◽

2007 ◽

Cited By ~ 1

Keyword(s):

Reinforcement Learning ◽

Problem Gambling ◽

Learning Models ◽

Behavioral Extinction ◽

Reinforcement Learning Models

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Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽

10.1037/dec0000040 ◽

2016 ◽

Vol 3 (2) ◽

pp. 115-131 ◽

Cited By ~ 14

Author(s):

Helen Steingroever ◽

Ruud Wetzels ◽

Eric-Jan Wagenmakers

Keyword(s):

Reinforcement Learning ◽

Iowa Gambling Task ◽

Bayes Factors ◽

Gambling Task ◽

Learning Models ◽

Reinforcement Learning Models

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Effects of Working Memory Capacity on the Speed and Accuracy of Learning in Reinforcement Learning Models

PsycEXTRA Dataset ◽

10.1037/e528942014-552 ◽

2014 ◽

Author(s):

Adnane Ez-Zizi ◽

Simon Farrell ◽

David Leslie

Keyword(s):

Working Memory ◽

Reinforcement Learning ◽

Working Memory Capacity ◽

Memory Capacity ◽

Learning Models ◽

Reinforcement Learning Models ◽

Speed And Accuracy

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Discriminating statistics for two-absorbing-barrier learning models

PsycEXTRA Dataset ◽

10.1037/e572342012-056 ◽

1964 ◽

Author(s):

Richard M. Rose

Keyword(s):

Learning Models

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Deciding when to escape a mine emergency: Modeling accumulation of evidence about emergencies through Instance-based Learning

PsycEXTRA Dataset ◽

10.1037/e578902012-182 ◽

2011 ◽

Author(s):

Varun Dutt ◽

Michael Yu ◽

Cleotilde Gonzalez

Keyword(s):

Instance Based Learning

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Supplemental Material for Reinforcement Learning Models of Risky Choice and the Promotion of Risk-Taking by Losses Disguised as Wins in Rats

Journal of Experimental Psychology Animal Learning and Cognition ◽

10.1037/xan0000141.supp ◽

2017 ◽

Keyword(s):

Reinforcement Learning ◽

Risk Taking ◽

Risky Choice ◽

Learning Models ◽

Losses Disguised As Wins ◽

Reinforcement Learning Models

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Improving XGBoost with Imagination Sampling

Communications of the Blyth Institute ◽

10.33014/issn.2640-5652.2.1.holloway.1 ◽

2020 ◽

Vol 2 (1) ◽

pp. 3-6

Author(s):

Eric Holloway

Keyword(s):

Machine Learning ◽

General System ◽

Learning Models ◽

Starting Point ◽

Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

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