Galkin O.A.
Algorithmic aspects of determining the depth functions in selecting the optimal hypothesis for data classification problems

This article investigates optimal hypothesis selection in classification problems on the basis of a class of hypotheses distributed with respect to a posteriori probability. A method is proposed based on the concepts of relative weighted average and depth functions acting in the space of classification functions. Algorithms are developed that approximate relative depths of data and relative weighted averages and provide polynomial approximations to half-space analogues. © 2016, Springer Science+Business Media New York.

Keywords: Bayes optimal hypothesis, relative depth function, weighted average, Approximation algorithms, Geometry, Polynomial approximation, A-posteriori probabilities, Algorithmic aspects, Bayes-optimal, Classification functions, Data classification problems, Half spaces, Weighted averages, Statistical methods

Cite:
Galkin O.A. (2016). Algorithmic aspects of determining the depth functions in selecting the optimal hypothesis for data classification problems. Cybernetics and Systems Analysis, 52 (5), 43-55. doi: https://doi.org/10.1007/s10559-016-9872-8 http://jnas.nbuv.gov.ua/article/UJRN-0000553517 [In Russian].