Black box optimization, machine lear...
Pardalos, Panos M.

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  • Black box optimization, machine learning, and no-free lunch theorems
  • 紀錄類型: 書目-電子資源 : Monograph/item
    正題名/作者: Black box optimization, machine learning, and no-free lunch theorems/ edited by Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis.
    其他作者: Pardalos, Panos M.
    出版者: Cham :Springer International Publishing : : 2021.,
    面頁冊數: x, 388 p. :ill., digital ;24 cm.
    內容註: Learning enabled constrained black box optimization (Archetti) -- Black-box optimization: Methods and applications (Hasan) -- Tuning algorithms for stochastic black-box optimization: State of the art and future perspectives (Bartz-Beielstein) -- Quality diversity optimization: A novel branch of stochastic optimization (Chatzilygeroudis) -- Multi-objective evolutionary algorithms: Past, present and future (Coello C.A) -- Black-box and data driven computation (Du) -- Mathematically rigorous global optimization and fuzzy optimization: A brief comparison of paradigms, methods, similarities and differences (Kearfott) -- Optimization under Uncertainty Explains Empirical Success of Deep Learning Heuristics (Kreinovich) -- Variable neighborhood programming as a tool of machine learning (Mladenovic) -- Non-lattice covering and quanitization of high dimensional sets (Zhigljavsky) -- Finding effective SAT partitionings via black-box optimization (Semenov) -- The No Free Lunch Theorem: What are its main implications for the optimization practice? ( Serafino) -- What is important about the No Free Lunch theorems? (Wolpert)
    Contained By: Springer Nature eBook
    標題: Machine learning - Mathematics. -
    電子資源: https://doi.org/10.1007/978-3-030-66515-9
    ISBN: 9783030665159
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W9402736 電子資源 11.線上閱覽_V 電子書 EB Q325.5 .B53 2021 一般使用(Normal) 在架 0
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