Entropy Randomization in Machine Learning
暫譯: 機器學習中的熵隨機化
Popkov, Yuri S., Popkov, Alexey Yu, Dubnov, Yuri A.
- 出版商: CRC
- 出版日期: 2022-08-09
- 售價: $3,950
- 貴賓價: 9.5 折 $3,753
- 語言: 英文
- 頁數: 392
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 1032306289
- ISBN-13: 9781032306285
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相關分類:
Machine Learning
海外代購書籍(需單獨結帳)
商品描述
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.
商品描述(中文翻譯)
《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（不確定的數據和研究對象的模型）下獲得最佳解。隨機化的機器學習程序涉及具有隨機參數的模型，以及在平衡條件下使用測量數據的模型參數的概率密度函數的最大熵估計。最佳性條件以具有積分成分的非線性方程的形式推導出來。為了以概率的方式解決這些方程，開發了一種新的數值隨機搜索方法。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、模擬地球人口的動態、預測電力供應系統的季節性電負荷波動，以及預測西西伯利亞的熱凍土湖面積。
特點：
- 系統性地呈現隨機化機器學習問題：從數據處理、結構化隨機模型和算法程序，到解決不同領域的應用相關問題
- 提供新的隨機全局優化數值方法和多維積分計算
- 一種通用的隨機化機器學習算法
本書將吸引專攻人工智慧和機器學習的本科生和研究生、參與應用機器學習系統開發的研究人員和工程師，以及各領域預測問題的研究人員。
作者簡介
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.
作者簡介(中文翻譯)
Yuri S. Popkov：工程博士，教授，俄羅斯科學院院士；俄羅斯科學院聯邦研究中心「計算機科學與控制」首席研究員；俄羅斯科學院Trapeznikov控制科學研究所首席研究員；莫斯科國立大學教授。著作超過250篇科學出版物，包括15部專著。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。
Alexey Yu. Popkov：科學候選人，俄羅斯科學院聯邦研究中心「計算機科學與控制」首席研究員；著作47篇科學出版物。他的研究興趣包括軟體工程、高效能計算、資料探勘、機器學習和熵方法。
Yuri A. Dubnov：物理學碩士，俄羅斯科學院聯邦研究中心「計算機科學與控制」研究員。著作超過18篇科學出版物。他的研究興趣包括機器學習、預測、隨機方法和貝葉斯估計。
