Comparative investigation on dimension reduction and regression in three layer feed-forward neural network
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AbstractThree layer feed-forward neural network (3-LFFNN) has been widely used for nonlinear regression. It is well known that its hidden layer can be regarded as taking the role of feature extraction and dimension reduction, and that the regression performance relies on how the feature dimension or equivalently the number of hidden units is determined appropriately. There are many publications on determining the hidden unit number for a desired generalization error. However, few comparative studies have been made on different approaches proposed, especially on those typical model selection criteria for this purpose. This paper targets such an aim. Using both simulated data and several real world data sets, a comparative study has been made on the regression performances with the number of hidden units determined by several typical model selection criteria, including Akaike's Information Criterion (AIC), the consistent Akaike's information criterion (CAIC), Schwarz's Bayesian Inference Criterion (BIC) which coincides with Rissanen's Minimum Description Length (MDL) criterion, and the well-known technique cross-validation (CV), as well as the Bayesian Ying-Yang harmony criterion on a small sample size (BYY-S). As shown in experiments on a small size of samples, BIC and CV are better than AIC and CATC obviously. Moreover, BIC may be better than CV on certain data sets, while CV may be better than BIC on other data sets. Interestingly, BYY-S generally outperforms both BIC and CV.
All Author(s) ListShi L, Xu L
Name of Conference16th International Conference on Artificial Neural Networks (ICANN 2006)
Start Date of Conference10/09/2006
End Date of Conference14/09/2006
Place of ConferenceAthens
Journal nameLecture Notes in Artificial Intelligence
Detailed descriptionorganized by National Technical University of Athens (NTUA),
Year2006
Month1
Day1
Volume Number4131
PublisherSPRINGER-VERLAG BERLIN
Pages51 - 60
ISBN3-540-38625-4
ISSN0302-9743
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods

Last updated on 2020-31-05 at 02:29