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Entropy 2017, 19(4), 178; doi:10.3390/e19040178

Entropy “2”-Soft Classification of Objects

1
Institute for Systems Analysis of Federal Research Center “Computer Science and Control”, Moscow 117312, Russia
2
Intelligent Technologies in System Analysis and Management, National Research University Higher School of Economics, Moscow 125319, Russia
3
Department of Software Engineering, ORT Braude College, Karmiel 2161002, Israel
*
Author to whom correspondence should be addressed.
Academic Editor: Dawn E. Holmes
Received: 10 March 2017 / Revised: 10 April 2017 / Accepted: 18 April 2017 / Published: 20 April 2017
(This article belongs to the Special Issue Maximum Entropy and Its Application II)
View Full-Text   |   Download PDF [1300 KB, uploaded 20 April 2017]   |  

Abstract

A proposal for a new method of classification of objects of various nature, named “2”-soft classification, which allows for referring objects to one of two types with optimal entropy probability for available collection of learning data with consideration of additive errors therein. A decision rule of randomized parameters and probability density function (PDF) is formed, which is determined by the solution of the problem of the functional entropy linear programming. A procedure for “2”-soft classification is developed, consisting of the computer simulation of the randomized decision rule with optimal entropy PDF parameters. Examples are provided. View Full-Text
Keywords: randomization; entropy; learning collection; machine learning; objects classification; randomized machine learning randomization; entropy; learning collection; machine learning; objects classification; randomized machine learning
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Popkov, Y.S.; Volkovich, Z.; Dubnov, Y.A.; Avros, R.; Ravve, E. Entropy “2”-Soft Classification of Objects. Entropy 2017, 19, 178.

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