Instance-based Learning Models of Training

2010 ◽  
Author(s):  
Cleotilde Gonzalez ◽  
Varun Dutt
Keyword(s):  
Learning Models ◽  
Instance Based Learning

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

Games ◽  
2011 ◽  
Vol 2 (1) ◽  
pp. 136-162 ◽  
Author(s):  
Cleotilde Gonzalez ◽  
Varun Dutt ◽  
Tomás Lejarraga
Keyword(s):  
Market Entry ◽  
Learning Models ◽  
Instance Based Learning

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

Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
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

Improving XGBoost with Imagination Sampling

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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