Paper Info
Title
Laws Describing Artificial Learning.
Abstract
The power law is predominantly describing the ways humans learn, especially in psychophysics, in skill acquisition, and in retention. Yet a few researchers claim that this law is applicable only on the aggregate level and that exponential law should be considered when describing a single learning process. The question which law should be used on aggregate or single learner level has not yet been answered in the artificial learning community. This work is shedding some light towards the answers. We conducted an experiment with three artificial learners using 109 training cases. The statistical tests have shown that power law and exponential law are describing the learning curves equally well. However, in quite many cases neither of laws is applicable. Additionally, there are significant differences among artificial learners.
Year
DOI
Venue
2016
10.3233/978-1-61499-720-7-274
Frontiers in Artificial Intelligence and Applications
Keywords
Field
DocType
machine learning,neural networks,support vector machines,learning curve,error rate,power law,exponential function
Discrete mathematics,Mathematics,Calculus
Conference
Volume
ISSN
Citations 
292
0922-6389
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Bostjan Brumen126025.48
Ivan Rozman2414122.20
Ales Cernezel323.06