DocumentCode
327646
Title
An empirical comparison of arc-cosine distance, generalised Fisher ratio and normalised entropy criteria for model selection
Author
Zheng, S. ; Molina, C.G.
Author_Institution
Dept. of Comput. Sci., Anglia Polytech. Univ., Cambridge, UK
fYear
1998
fDate
31 Aug-2 Sep 1998
Firstpage
214
Lastpage
223
Abstract
Three model selection criteria, the arc-cosine distance, the generalised Fisher ratio and the normalised entropy, are applied to several data sets sampled from different mixture models. Their performance is investigated and their ability to measure the mutual information between the components in a mixture model is compared. Experimental results show that the arc-cosine distance criterion outperforms the other two criteria
Keywords
entropy; modelling; pattern recognition; arc-cosine distance; data sets; generalised Fisher ratio; mixture models; model partitioning; model selection; normalised entropy criteria; Bayesian methods; Computer science; Entropy; Gaussian distribution; Monte Carlo methods; Mutual information; Partitioning algorithms; Performance evaluation;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks for Signal Processing VIII, 1998. Proceedings of the 1998 IEEE Signal Processing Society Workshop
Conference_Location
Cambridge
ISSN
1089-3555
Print_ISBN
0-7803-5060-X
Type
conf
DOI
10.1109/NNSP.1998.710651
Filename
710651
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