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Libros sobre el tema "Bid Optimization"

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Autor: Grafiati
Publicado: 6 de septiembre de 2023

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1

Pardalos, Panos, Mario Pavone, Giovanni Maria Farinella y Vincenzo Cutello, eds. Machine Learning, Optimization, and Big Data. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27926-8.

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2

Nicosia, Giuseppe, Panos Pardalos, Giovanni Giuffrida y Renato Umeton, eds. Machine Learning, Optimization, and Big Data. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72926-8.

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3

Pardalos, Panos M., Piero Conca, Giovanni Giuffrida y Giuseppe Nicosia, eds. Machine Learning, Optimization, and Big Data. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51469-7.

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4

Emrouznejad, Ali, ed. Big Data Optimization: Recent Developments and Challenges. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30265-2.

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5

Nijdam, Jelle Luutzen. Behaviour and optimization of packed bed regenerators. Eindhoven: Eindhoven University of Technology, 1995.

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6

Wang, John. Encyclopedia of business analytics and optimization. Hershey, PA: Business Science Reference, 2014.

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7

Zhan, Jianfeng, Rui Han y Chuliang Weng, eds. Big Data Benchmarks, Performance Optimization, and Emerging Hardware. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13021-7.

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8

Zhan, Jianfeng, Rui Han y Roberto V. Zicari, eds. Big Data Benchmarks, Performance Optimization, and Emerging Hardware. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29006-5.

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9

Choi, Tsan-Ming, Jianjun Gao, James H. Lambert, Chi-Kong Ng y Jun Wang, eds. Optimization and Control for Systems in the Big-Data Era. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53518-0.

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10

1946-, Elshishini S. S., ed. Modelling, simulation, and optimization of industrial fixed bed catalytic reactors. Yverdon, Switzerland: Gordon and Breach Science Publishers, 1993.

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11

Petrucci, Alessandra y Rosanna Verde, eds. SIS 2017. Statistics and Data Science: new challenges, new generations. Florence: Firenze University Press, 2017. http://dx.doi.org/10.36253/978-88-6453-521-0.

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The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data.
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12

Gerhard, Kreysa, Dechema y Society of Chemical Industry (Great Britain). Elecrtrochemical Technology Group., eds. Electrochemical cell design and optimization procedures: Papers of the conference Bad Soden, September 24-26, 1990. Weinheim: VCH, 1991.

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13

P, Rowe Sean, Breininger David R y United States. National Aeronautics and Space Administration., eds. Temporal, spatial, and diurnal patterns in avian activity at the Shuttle Landing Facility, John F. Kennedy Space Center, Florida, U.S.A. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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14

Advanced BDD Optimization. Berlin/Heidelberg: Springer-Verlag, 2005. http://dx.doi.org/10.1007/b107399.

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15

Drechsler, Rolf, Rüdiger Ebendt y Görschwin Fey. Advanced BDD Optimization. Springer London, Limited, 2005.

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16

Advanced BDD Optimization. Springer, 2005.

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17

Drechsler, Rolf, Görschwin Fey y Rudiger Ebendt. Advanced BDD Optimization. Springer, 2010.

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18

Dhaenens, Clarisse y Laetitia Jourdan. Metaheuristics for Big Data. Wiley & Sons, Incorporated, John, 2016.

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19

Metaheuristics for Big Data. Wiley & Sons, Incorporated, John, 2016.

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20

Dhaenens, Clarisse y Laetitia Jourdan. Metaheuristics for Big Data. Wiley & Sons, Incorporated, John, 2016.

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21

Dhaenens, Clarisse y Laetitia Jourdan. Metaheuristics for Big Data. Wiley & Sons, Incorporated, John, 2016.

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22

Vasant, Pandian, J. Joshua Thomas, Pinar Karagoz y B. Bazeer Ahamed. Deep Learning Techniques and Optimization Strategies in Big Data Analytics. IGI Global, 2019.

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23

Vasant, Pandian, J. Joshua Thomas, Pinar Karagoz y B. Bazeer Ahamed. Deep Learning Techniques and Optimization Strategies in Big Data Analytics. IGI Global, 2019.

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24

Vasant, Pandian, J. Joshua Thomas, Pinar Karagoz y B. Bazeer Ahamed. Deep Learning Techniques and Optimization Strategies in Big Data Analytics. IGI Global, 2019.

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25

Vasant, Pandian, J. Joshua Thomas, Pinar Karagoz y B. Bazeer Ahamed. Deep Learning Techniques and Optimization Strategies in Big Data Analytics. IGI Global, 2019.

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26

Vasant, Pandian, J. Joshua Thomas, Pinar Karagoz y B. Bazeer Ahamed. Deep Learning Techniques and Optimization Strategies in Big Data Analytics. IGI Global, 2019.

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27

Emrouznejad, Ali. Big Data Optimization: Recent Developments and Challenges. Springer, 2016.

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28

Emrouznejad, Ali. Big Data Optimization: Recent Developments and Challenges. Springer, 2018.

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29

Emrouznejad, Ali. Big Data Optimization: Recent Developments and Challenges. Springer London, Limited, 2016.

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30

Lopez, J. ADVANCED OPTIMIZATION with MATLAB Using BIG DATA TECHNIQUES. Independently Published, 2019.

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31

Han, Rui, Jianfeng Zhan y Chuliang Weng. Big Data Benchmarks, Performance Optimization, and Emerging Hardware. Springer, 2014.

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32

Stochastic Optimization for Large-Scale Machine Learning. Taylor & Francis Group, 2021.

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33

Chauhan, Vinod Kumar. Stochastic Optimization for Large-Scale Machine Learning. Taylor & Francis Group, 2021.

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34

Chauhan, Vinod Kumar. Stochastic Optimization for Large-Scale Machine Learning. CRC Press LLC, 2021.

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35

Chauhan, Vinod Kumar. Stochastic Optimization for Large-Scale Machine Learning. Taylor & Francis Group, 2021.

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36

Jhaveri, Rutvij H., Victor Hugo C. de Albuquerque, Akash Kumar Bhoi y Ranjit Panigrahi. Healthcare Big Data Analytics: Computational Optimization and Cohesive Approaches. de Gruyter GmbH, Walter, 2022.

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37

Jhaveri, Rutvij H., Victor Hugo C. de Albuquerque, Akash Kumar Bhoi y Ranjit Panigrahi. Healthcare Big Data Analytics: Computational Optimization and Cohesive Approaches. de Gruyter GmbH, Walter, 2022.

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38

Jhaveri, Rutvij H., Victor Hugo C. de Albuquerque, Akash Kumar Bhoi y Ranjit Panigrahi. Healthcare Big Data Analytics: Computational Optimization and Cohesive Approaches. de Gruyter GmbH, Walter, 2022.

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39

Lopez, J. OPTIMIZATION with MATLAB. QUADRATIC PROGRAMMING, LEAST SQUARES, SYSTEMS of EQUATIONS, PROBLEM-BASED and BIG DATA for OPTIMIZATION. Independently Published, 2019.

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40

S. S. E. H. Elnashaie. Modelling, Simulation and Optimization of Industrial Fixed Bed Catalytic Reactors. CRC Press LLC, 2021.

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41

S. S. E. H. Elnashaie. Modelling, Simulation and Optimization of Industrial Fixed Bed Catalytic Reactors. CRC Press LLC, 2021.

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42

S. S. E. H. Elnashaie. Modelling, Simulation and Optimization of Industrial Fixed Bed Catalytic Reactors. CRC Press LLC, 2021.

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43

S. S. E. H. Elnashaie. Modelling, Simulation and Optimization of Industrial Fixed Bed Catalytic Reactors. CRC Press LLC, 2021.

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44

Namatame, Akira y Takanori Komatsu. Modeling of Desirable Socioeconomic Networks. Editado por Shu-Heng Chen, Mak Kaboudan y Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.15.

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This chapter discusses the issues of designing desirable socioeconomic networks. Such networks permeate our lives. Evidence of this has generated increasing interest in dynamic processes in complex networks, especially the interplay between processes and the influences of network structure on performance and robustness. Performance optimization and robustness are important issues of socioeconomic networks. Diffusion is the process by which new products are invented and successfully introduced into a society (good diffusion) or infectious diseases spread (bad diffusion). Many studies shed light on how network topology interacts with the structure of social networked systems such as financial institutions to determine systemwide crises. In this context, entire classes of optimization problems range from maximizing the diffusion of innovations to minimizing risk distributions and cascade failures. The structure of interconnections influences network performance.
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45

Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast. MDPI, 2021. http://dx.doi.org/10.3390/books978-3-0365-0863-4.

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46

Send, Wolfgang. Winged artifacts. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0046.

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Winged artifacts aim at the imitation of nature’s ingenious method to produce thrust with slim and smart-shaped flapping surfaces—the bending-torsional drive. The kinematics of these surfaces shows, in three dimensions, a bending motion coupled with a simultaneous torsion. This chapter describes the design and development of the artificial bird SmartBird, which was introduced in 2011 on the occasion of the annual international industry fair Hannovermesse. This artwork with articulated wings received worldwide attention through its unprecedented agility. The efficient motion of bodies heavier than air rests on the optimization of target functions like total weight to be balanced by lift, flow resistance to be balanced by thrust, structural layout and reliability, energy storage and, last but not least, smart flight control. From the author’s point of view, the bending-torsional drive just has started its career as a new player in this optimization game.
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47

Pardalos, P. M. Machine Learning, Optimization, and Big Data: Third International Conference, MOD 2017, Volterra, Italy, September 14–17, 2017, Revised Selected Papers. Springer, 2017.

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48

Wang, Jun, Jianjun Gao, Tsan-Ming Choi, James H. Lambert y Chi-Kong Ng. Optimization and Control for Systems in the Big-Data Era: Theory and Applications. Springer, 2017.

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49

Wang, Jun, Jianjun Gao, Tsan-Ming Choi, James H. Lambert y Chi-Kong Ng. Optimization and Control for Systems in the Big-Data Era: Theory and Applications. Springer, 2018.

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50

Pardalos, P. M. Machine Learning, Optimization, and Big Data: First International Workshop, MOD 2015, Taormina, Sicily, Italy, July 21-23, 2015, Revised Selected Papers. Springer London, Limited, 2016.

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