Entropy Randomization in Machine Learning by Yuri S. Popkov (English) Paperback

Description: Entropy Randomization in Machine Learning by Yuri S. Popkov, Alexey Yu. Popkov, Yuri A. Dubnov Entropy Randomization in Machine Learning presents a new approach to machine learning - entropy randomization - to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy randomization—to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with random parameters and maximum entropy estimates of the probability density functions of the model parameters under balance conditions with measured data. Optimality conditions are derived in the form of nonlinear equations with integral components. A new numerical random search method is developed for solving these equations in a probabilistic sense. Along with the theoretical foundations of randomized machine learning, Entropy Randomization in Machine Learning considers several applications to binary classification, modelling the dynamics of the Earths population, predicting seasonal electric load fluctuations of power supply systems, and forecasting the thermokarst lakes area in Western Siberia. Features• A systematic presentation of the randomized machine-learning problem: from data processing, through structuring randomized models and algorithmic procedure, to the solution of applications-relevant problems in different fields • Provides new numerical methods for random global optimization and computation of multidimensional integrals• A universal algorithm for randomized machine learningThis book will appeal to undergraduates and postgraduates specializing in artificial intelligence and machine learning, researchers and engineers involved in the development of applied machine learning systems, and researchers of forecasting problems in various fields. Author Biography 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. Table of Contents 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 Details ISBN1032307749 Author Yuri A. Dubnov Language English ISBN-10 1032307749 ISBN-13 9781032307749 Pages 392 Publisher Taylor & Francis Ltd Series Chapman & Hall/CRC Machine Learning & Pattern Recognition Year 2024 Format Paperback Publication Date 2024-10-08 Imprint Chapman & Hall/CRC Country of Publication United Kingdom Alternative 9781032306285 DEWEY 006.31 Illustrations 17 Tables, black and white; 159 Line drawings, black and white; 159 Illustrations, black and white Audience Tertiary & Higher Education UK Release Date 2024-10-08 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:161475687;

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