How to infer the informational energy from small datasets
Document Type
Conference Proceeding
Department or Administrative Unit
Computer Science
Publication Date
5-24-2012
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.
Recommended Citation
Cafaron, A., & Andonie, R. (2012). How to infer the informational energy from small datasets. 2012 13th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). https://doi.org/10.1109/optim.2012.6231921
Journal
2012 13th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM)
Rights
Copyright © 2012, IEEE
Comments
This article was originally published in 2012 13th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). The full-text article from the publisher can be found here.
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