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Searching Most Efficient Neural Network Architecture Using Akaike's Information Criterion (AIC)

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International Journal of Computer Applications
© 2010 by IJCA Journal
Number 5 - Article 6
Year of Publication: 2010
Authors:
Gaurang Panchal
Amit Ganatra
Y.P.Kosta
Devyani Panchal
10.5120/126-242

Gaurang Panchal, Amit Ganatra, Y.P.Kosta and Devyani Panchal. Article: Searching Most Efficient Neural Network Architecture Using Akaike's Information Criterion (AIC). International Journal of Computer Applications 1(5):41–44, February 2010. Published By Foundation of Computer Science. BibTeX

@article{key:article,
	author = {Gaurang Panchal and Amit Ganatra and Y.P.Kosta and Devyani Panchal},
	title = {Article: Searching Most Efficient Neural Network Architecture Using Akaike's Information Criterion (AIC)},
	journal = {International Journal of Computer Applications},
	year = {2010},
	volume = {1},
	number = {5},
	pages = {41--44},
	month = {February},
	note = {Published By Foundation of Computer Science}
}

Abstract

The problem of model selection is considerably important for acquiring higher levels of generalization capability in supervised learning. Neural networks are commonly used networks in many engineering applications due to its better generalization property. An ensemble neural network algorithm is proposed based on the Akaike information criterion (AIC). Ecologists have long relied on hypothesis testing to include or exclude variables in models, although the conclusions often depend on the approach used. The advent of methods based on information theory, also known as information-theoretic approaches, has changed the way we look at model selection The Akaike information criterion (AIC) has been successfully used in model selection. It is not easy to decide the optimal size of the neural network because of its strong nonlinearity. We discuss problems with well used information and propose a model selection method.

Reference

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