Entropy “2”-Soft Classification of Objects

Popkov, Yuri S.; Volkovich, Zeev; Dubnov, Yuri A.; Avros, Renata; Ravve, Elena V.

doi:10.3390/e19040178

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The concept and computational procedure of Randomized Machine Learning proposed in [22] turned out to be very useful in terms of inaccurate data estimation probability distributions, and also an effective computer technique for solving many applied problems [24]. The modules of this procedure have been applied to practical problems of the randomized forecasting of World population [32], electrical load in the power systems [33], the evolution of the thermokarst lakes in the Arctic zone [34], randomized classification of the objects [35,36]. In these works, we used public datasets of the UN [37], and [38].…”

Section: Discussionmentioning

confidence: 99%

Reinforcement Procedure for Randomized Machine Learning

Popkov,

Dubnov,

Popkov

2023

Mathematics

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This paper is devoted to problem-oriented reinforcement methods for the numerical implementation of Randomized Machine Learning. We have developed a scheme of the reinforcement procedure based on the agent approach and Bellman’s optimality principle. This procedure ensures strictly monotonic properties of a sequence of local records in the iterative computational procedure of the learning process. The dependences of the dimensions of the neighborhood of the global minimum and the probability of its achievement on the parameters of the algorithm are determined. The convergence of the algorithm with the indicated probability to the neighborhood of the global minimum is proved.

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Section: Discussionmentioning

confidence: 99%

Reinforcement Procedure for Randomized Machine Learning

Popkov,

Dubnov,

Popkov

2023

Mathematics

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“…As a result of training, the model is equipped with entropy-optimal estimates of the parameter and measurement noise distributions, thus forming a randomized predictive model . This model defines a special forecasting technique randomized forecasting, the elements of which were used for some applied problems [24][25][26].…”

Section: Randomized Forecastingmentioning

confidence: 99%

Randomized Machine Learning of Nonlinear Models with Application to Forecasting the Development of an Epidemic Process

Popkov

2021

Autom Remote Control

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We develop a discrete approach in the theory of randomized machine learning that is aimed at application to nonlinear models. We formulate the problem of entropy estimation of probability distributions and measurement noise for discrete nonlinear models. Issues related to the application of such models to forecasting problems, in particular, the problem of generating entropy-optimal distributions, are considered. The proposed methods are demonstrated on the solution of the problem of forecasting the total number of persons infected with novel coronavirus SARS-CoV-2 in Germany in 2020.

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Forecasting Development of COVID-19 Epidemic in European Union Using Entropy-Randomized Approach

Popkov¹,

Dubnov²,

Popkov³

2021

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The paper is devoted to the forecasting of the COVID-19 epidemic by the novel method of randomized machine learning. This method is based on the idea of estimation of probability distributions of model parameters and noises on real data. Entropy-optimal distributions correspond to the state of maximum uncertainty which allows the resulting forecasts to be used as forecasts of the most "negative" scenario of the process under study. The resulting estimates of parameters and noises, which are probability distributions, must be generated, thus obtaining an ensemble of trajectories that considered to be analyzed by statistical methods. In this work, for the purposes of such an analysis, the mean and median trajectories over the ensemble are calculated, as well as the trajectory corresponding to the mean over distribution values of the model parameters. The proposed approach is used to predict the total number of infected people using a three-parameter logistic growth model. The conducted experiment is based on real COVID-19 epidemic data in several countries of the European Union. The main goal of the experiment is to demonstrate an entropy-randomized approach for predicting the epidemic process based on real data near the peak. The significant uncertainty contained in the available real data is modeled by an additive noise within 30%, which is used both at the training and predicting stages. To tune the hyperparameters of the model, the scheme is used to configure them according to a testing dataset with subsequent retraining of the model. It is shown that with the same datasets, the proposed approach makes it possible to predict the development of the epidemic more efficiently in comparison with the standard approach based on the least-squares method.

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Entropy “2”-Soft Classification of Objects

Cited by 3 publications

References 11 publications

Reinforcement Procedure for Randomized Machine Learning

Reinforcement Procedure for Randomized Machine Learning

Randomized Machine Learning of Nonlinear Models with Application to Forecasting the Development of an Epidemic Process

Forecasting Development of COVID-19 Epidemic in European Union Using Entropy-Randomized Approach

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