Search Results - basic (differences OR different) (relocation OR relation) algorithms

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    Metrology

    Published 2023
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    X-Machines for Agent-Based Modeling by Kiran, Mariam

    Published 2025
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    Tensor Network Contractions by Ran, Shi-Ju, Tirrito, Emanuele, Peng, Cheng, Chen, Xi, Tagliacozzo, Luca, Su, Gang, Lewenstein, Maciej

    Published 2021
    “…To fill this gap, this book explains the fundamental concepts and basic ideas that connect and/or unify different strategies of the tensor network contraction algorithms. …”
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    Advances in Near Infrared Spectroscopy and Related Computational Methods by Bec, Krzysztof B., Huck, Christian

    Published 2021
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    Entropy Measures for Data Analysis: Theory, Algorithms and Applications by Keller, Karsten

    Published 2021
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    The Foundation of Precision Medicine: Integration of Electronic Health Records with Nenomics Through Basic, Clinical, and Translational Research by Mariza de Andrade, Helena Kuivaniemi, Marylyn D. Ritchie

    Published 2021
    “…Chapter 2 describes the results of genetic studies on different diseases for which all the phenotypic information was extracted from the EHR with highly specific ePhenotyping algorithms. …”
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    Individual differences in associative learning by Rachel M. Msetfi, Robin A. Murphy

    Published 2021
    “…This work involves varying stimulus properties and temporal relations or modeling the differences between groups.…”
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    Discrete Event Simulations

    Published 2021
    “…The range of application of DES spans across many different disciplines and research fields. In research, further development and advancements of the basic DES algorithm continue to be sought while various hybrid methods derived by combining DES with other simulation techniques continue to be developed. …”
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    Uncertain Multi-Criteria Optimization Problems

    Published 2022
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    Metalearning by Brazdil, Pavel, van Rijn, Jan N., Soares, Carlos, Vanschoren, Joaquin

    Published 2022
    “…This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. …”
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