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Entropy “2”-Soft Classification of Objects

Institute for Systems Analysis of Federal Research Center “Computer Science and Control”, Moscow 117312, Russia
Intelligent Technologies in System Analysis and Management, National Research University Higher School of Economics, Moscow 125319, Russia
Department of Software Engineering, ORT Braude College, Karmiel 2161002, Israel
Author to whom correspondence should be addressed.
Academic Editor: Dawn E. Holmes
Entropy 2017, 19(4), 178;
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)
PDF [1300 KB, uploaded 20 April 2017]


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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