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