Authors:
Seiya Satoh
1
and
Ryohei Nakano
2
Affiliations:
1
National Institute of Advanced Industrial Science and Tech, Japan
;
2
Chubu University, Japan
Keyword(s):
Information Criteria, Model Selection, Multilayer Perceptron, Singular Model.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Model Selection
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Regression
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
The present paper evaluates newly invented information criteria for singular models. Well-known criteria such as AIC and BIC are valid for regular statistical models, but their validness for singular models is not guaranteed. Statistical models such as multilayer perceptrons (MLPs), RBFs, HMMs are singular models. Recently WAIC and WBIC have been proposed as new information criteria for singular models. They are developed on a strict mathematical basis, and need empirical evaluation. This paper experimentally evaluates how WAIC and WBIC work for MLP model selection using conventional and new learning methods.