How to infer the informational energy from small datasets

Authors

Angel Cataron, Transilvania University of Braşov
Răzvan Andonie, Central Washington UniversityFollow

Department or Administrative Unit

Computer Science

Document Type

Conference Proceeding

Author Copyright

Copyright © 2012, IEEE

Publication Date

5-24-2012

Journal

2012 13th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM)

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.

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.

Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.

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