תוצאות חיפוש - (( basic difference relation algorithm ) OR ( basic diferentes evolution algorithms ))

Metrology

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X-Machines for Agent-Based Modeling מאת Kiran, Mariam

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Inteligencia Artificial Aplicada a Procesamiento de Lenguaje Natural (NLP) con Python y Machine Learning. מאת Sangacha Tapia, Lady Mariuxi, Celi, Ricardo Javier, Acosta Guzmán, Ivan Leonel, Varela Tapia, Eleanor Alexandra

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

“...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 מאת Bec, Krzysztof B., Huck, Christian

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Entropy Measures for Data Analysis: Theory, Algorithms and Applications מאת Keller, Karsten

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

“...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 מאת Rachel M. Msetfi, Robin A. Murphy

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

“...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

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Metalearning מאת Brazdil, Pavel, van Rijn, Jan N., Soares, Carlos, Vanschoren, Joaquin

“...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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