Entropy Randomization in Machine Learning

Yuri S. Popkov: Doctor of Engineering, Professor, Academician of Russian Academy of Sciences; Chief Researcher at Federal Research Center “Computer Science and Control,” Russian Academy of Sciences; Chief Researcher at Trapeznikov Institute of Control Sciences, Russian Academy of Sciences; Professor at Lomonosov Moscow State University. Author of more than 250 scientific publications, including 15 monographs. His research interests include stochastic dynamic systems, optimization, machine learning, and macrosystem modeling.Alexey Yu. Popkov: Candidate of Sciences, Leading Researcher at Federal Research Center “Computer Science and Control,” Russian Academy of Sciences; author of 47 scientific publications. His research interests include software engineering, high-performance computing, data mining, machine learning, and entropy methods.Yuri A. Dubnov: MSc in Physics, Researcher at Federal Research Center “Computer Science and Control,” Russian Academy of Sciences. Author of more than 18 scientific publications. His research interests include machine learning, forecasting, randomized approaches, and Bayesian estimation.

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• Preface1. General Concept of Machine Learning2. Data Sources and Models Chapter3. Dimension Reduction Methods4. Randomized Parametric Models5. Entropy-robust Estimation Procedures for Randomized Models and Measurement Noises6. Entropy-Robust Estimation Methods for Probabilities of Belonging in Machine Learning Procedures7. Computational Methods od Randomized Machine Learning 8. Generation Methods for Random Vectors with Given Probability Density Functions over Compact Sets9. Information Technologies of Randomized Machine Learning10. Entropy Classification11. Randomized Machine Learning in Problems of Dynamic Regression and PredictionAppendix A: Maximum Entropy Estimate (MEE) and Its Asymptotic EfficiencyAppendix B: Approximate Estimation of Structural Characteristics of Linear Dynamic Regression Model (LDR)Bibliography