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Titlebook: Black Box Optimization, Machine Learning, and No-Free Lunch Theorems; Panos M. Pardalos,Varvara Rasskazova,Michael N. Vr Book 2021 Springe

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發(fā)表于 2025-3-21 16:28:12 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Black Box Optimization, Machine Learning, and No-Free Lunch Theorems
影響因子2023Panos M. Pardalos,Varvara Rasskazova,Michael N. Vr
視頻videohttp://file.papertrans.cn/189/188926/188926.mp4
發(fā)行地址Beginners will achieve an overview of optimization methods.Researchers will gain access to to a useful reference on key topics.Mathematical rigour and heuristics approaches equip the reader with diffe
學(xué)科分類Springer Optimization and Its Applications
圖書封面Titlebook: Black Box Optimization, Machine Learning, and No-Free Lunch Theorems;  Panos M. Pardalos,Varvara Rasskazova,Michael N. Vr Book 2021 Springe
影響因子.This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual applications and problems. ?Current theoretical, algorithmic, and practical methods used are provided to stimulate a new effort towards innovative and efficient solutions.?The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods. The coverage ranges from mathematically rigorous methods to heuristic and evolutionary approaches in an attempt to equip the reader with different viewpoints of the same problem..
Pindex Book 2021
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978-3-030-66517-3Springer Nature Switzerland AG 2021
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地板
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Black-Box and Data-Driven Computation,exponential growth in big data recently, data-driven computation has utilized black box as a tool for proving solutions to some computational problems. In this note, we present several observations on this new role of black box using reduction techniques in the computational complexity theory.
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發(fā)表于 2025-3-22 10:59:42 | 只看該作者
Mathematically Rigorous Global Optimization and Fuzzy Optimization,of the tools used in implementations are similar or identical. We review, compare and contrast basic ideas and applications behind these areas, referring to some of the work in the very large literature base.
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Black Box Optimization, Machine Learning, and No-Free Lunch Theorems978-3-030-66515-9Series ISSN 1931-6828 Series E-ISSN 1931-6836
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Grundlagen der Schwingungslehre,exponential growth in big data recently, data-driven computation has utilized black box as a tool for proving solutions to some computational problems. In this note, we present several observations on this new role of black box using reduction techniques in the computational complexity theory.
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