Entropy Randomization in Machine Learning

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 Earth's 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 learning

This 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.

《機器學習中的熵隨機化》提出了一種新的機器學習方法-熵隨機化，以在不確定性條件下（不確定的數據和對象模型）獲得最佳解決方案。隨機化的機器學習程序涉及具有隨機參數的模型，以及在平衡條件下對模型參數的概率密度函數進行最大熵估計。最優條件以非線性方程的形式推導出來，其中包含積分成分。還開發了一種新的數值隨機搜索方法，以概率意義上解決這些方程。除了隨機化機器學習的理論基礎外，《機器學習中的熵隨機化》還考慮了幾個應用，包括二元分類、建模地球人口動態、預測電力供應系統的季節性電負載波動，以及預測西伯利亞西部的熱融湖區域。

特點：
- 對隨機化機器學習問題的系統性介紹：從數據處理、結構化隨機化模型和算法程序，到解決不同領域的應用相關問題
- 提供了用於隨機全局優化和多維積分計算的新數值方法
- 一種通用的隨機化機器學習算法

本書適合專攻人工智能和機器學習的本科生和研究生、從事應用機器學習系統開發的研究人員和工程師，以及各個領域的預測問題研究人員。

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.

尤里·S·波普科夫（Yuri S. Popkov）：工程博士、教授、俄羅斯科學院院士；俄羅斯科學院「計算機科學與控制」聯邦研究中心首席研究員；俄羅斯科學院「控制科學」特拉佩茲尼科夫研究所首席研究員；莫斯科國立大學教授。發表過250多篇科學論文，包括15本專著。他的研究興趣包括隨機動態系統、優化、機器學習和宏系統建模。

亞歷克謝·尤·波普科夫（Alexey Yu. Popkov）：科學候選人，俄羅斯科學院「計算機科學與控制」聯邦研究中心領先研究員；發表過47篇科學論文。他的研究興趣包括軟體工程、高性能計算、數據挖掘、機器學習和熵方法。

尤里·A·杜布諾夫（Yuri A. Dubnov）：物理學碩士，俄羅斯科學院「計算機科學與控制」聯邦研究中心研究員。發表過18多篇科學論文。他的研究興趣包括機器學習、預測、隨機方法和貝葉斯估計。