DocumentCode :
3647271
Title :
How to infer the informational energy from small datasets
Author :
Angel Cataron;Răzvan Andonie
Author_Institution :
Transilvania University of Braş
fYear :
2012
fDate :
5/1/2012 12:00:00 AM
Firstpage :
1065
Lastpage :
1070
Abstract :
Motivated by the problems in machine learning, we introduce a novel non-parametric estimator of Onicescu´s informational energy. Our method is based on the k-th nearest neighbor distances between the n sample points, where k is a fixed positive integer. For some standard distributions, we investigate the performance of the estimator for small datasets.
Keywords :
"Training","Approximation methods","Estimation","Complexity theory","Entropy","Gaussian distribution","Random variables"
Publisher :
ieee
Conference_Titel :
Optimization of Electrical and Electronic Equipment (OPTIM), 2012 13th International Conference on
ISSN :
1842-0133
Print_ISBN :
978-1-4673-1650-7
Type :
conf
DOI :
10.1109/OPTIM.2012.6231921
Filename :
6231921
Link To Document :
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