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Książki na temat „Hyperparameter selection and optimization”

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Autor: Grafiati
Data publikacji: 25 maja 2024

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1

Agrawal, Tanay. Hyperparameter Optimization in Machine Learning. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6579-6.

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2

Zheng, Minrui. Spatially Explicit Hyperparameter Optimization for Neural Networks. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5399-5.

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3

Pappalardo, Elisa, Panos M. Pardalos i Giovanni Stracquadanio. Optimization Approaches for Solving String Selection Problems. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9053-1.

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4

Li︠a︡tkher, V. M. Wind power: Turbine design, selection, and optimization. Hoboken, New Jersey: Scrivener Publishing, Wiley, 2014.

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5

East, Donald R. Optimization technology for leach and liner selection. Littleton, CO: Society of Mining Engineers, 1987.

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6

Zheng, Maosheng, Haipeng Teng, Jie Yu, Ying Cui i Yi Wang. Probability-Based Multi-objective Optimization for Material Selection. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-3351-6.

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7

Membranes for membrane reactors: Preparation, optimization, and selection. Chichester, West Sussex: Wiley, 2011.

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8

Zheng, Maosheng, Jie Yu, Haipeng Teng, Ying Cui i Yi Wang. Probability-Based Multi-objective Optimization for Material Selection. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-3939-8.

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9

Toy, Ayhan Özgür. Route, aircraft prioritization and selection for airlift mobility optimization. Monterey, Calif: Naval Postgraduate School, 1996.

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10

S, Handen Jeffrey, red. Industrialization of drug discovery: From target selection through lead optimization. New York: Dekker/CRC Press, 2005.

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11

Boyle, Phelim P. Optimal portfolio selection with transaction costs. Toronto: University of Toronto, Dept. of Statistics, 1994.

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12

The algebra of organic synthesis: Green metrics, design strategy, route selection, and optimization. Boca Raton: CRC Press, 2012.

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13

Baltes, Paul B. The measurement of selection, optimization, and compensation (SOC) by self report: Technical report 1999. Berlin: Max-Planck-Institut für Bildungsforschung, 1999.

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14

Balls, Malcolm. The optimal selection of turbine-generators for tidal power projects and the optimization of their operation. Salford: University of Salford, 1988.

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15

Efficient asset management: A practical guide to stock portfolio optimization and asset allocation. Boston, Mass: Harvard Business School Press, 1998.

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16

SLSFS 2005 (2005 Bohinj, Slovenia). Subspace, latent structure and feature selection: Statistical and Optimization Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005 : revised selected papers. Berlin: Springer, 2006.

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17

Mischenko, Aleksandr. Optimization models of investment management in logistics. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1839692.

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Streszczenie:
The textbook is devoted to planning and modeling of investment projects for the development of logistics infrastructure and supply chain management. Both theoretical models of investment management in logistics and practical examples of the use of the proposed methodology in the selection of optimal production programs of the enterprise, management of wholesale purchases of trading firms, analysis of the effectiveness of projects for the creation of warehouse infrastructure, working capital management in the procurement of material resources in industrial logistics are presented. For undergraduate and graduate students specializing in logistics and supply chain management.
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18

Cao, Bing-Yuan. Optimal Models and Methods with Fuzzy Quantities. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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19

Brodeckiy, Gennadiy, Denis Gusev i Ivan Shidlovskiy. Multi-criteria choice in logistics research. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1902741.

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Methods and models of optimization of solutions under many criteria are considered, which are conditioned by modern tasks to improve the operation of supply chains and logistics systems. Attention is paid to their specifics in relation to the tasks of organizing transport supply support. The anomalous phenomena of "blindness" to the indicators of individual particular criteria and the phenomena of "blocking" the choice of alternatives when optimizing such systems are analyzed. Special modifications of traditional selection criteria for optimization are presented, allowing to eliminate these phenomena in order to more effectively adapt the selected best solution to the preferences of the decision-maker. The analysis of filtering possibilities of the initially specified alternatives is given so that optimization procedures can be implemented in real time. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students of institutions of higher education. It can be useful for graduate students, logisticians and entrepreneurs who want to organize their business more efficiently.
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20

Efremov, German. Modeling of chemical and technological processes. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1090526.

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In an accessible form, the textbook presents the theoretical foundations of physical and mathematical modeling; considers the modeling of mass, heat and momentum transfer processes, the relationship and analogy between them; studies the theory of similarity, its application in modeling, models of the structure of flows in apparatuses. Experimental-statistical and experimental-analytical modeling methods are also described, which include "black box" methods, planning passive, active full and fractional factor experiments, and adjusting models based on the results of the experiment. At the same time, modeling of chemical reactors, methods of optimization of chemical-technological processes, their selection, comparison and application examples are considered. Examples of modeling and optimization of processes in chemical, petrochemical and biotechnology on a computer in Excel and MathCAD environments are given. The appendices provide the basics of working in the MathCAD environment and elements of matrix algebra. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is intended for bachelors who are trained for the chemical, petrochemical, food, textile and light industries. It can be useful for specialists and undergraduates, as well as for scientists, engineers and postgraduates dealing with the problem under consideration.
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21

Petrucci, Alessandra, i Rosanna Verde, red. 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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22

Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms. Packt Publishing, 2021.

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23

Zheng, Minrui. Spatially Explicit Hyperparameter Optimization for Neural Networks. Springer, 2022.

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24

Zheng, Minrui. Spatially Explicit Hyperparameter Optimization for Neural Networks. Springer Singapore Pte. Limited, 2021.

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25

Agrawal, Tanay. Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient. Apress L. P., 2020.

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26

Agarwal, Saurabh. Portfolio Selection Using Multi-Objective Optimization. Springer International Publishing AG, 2017.

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27

Lyatkher, Victor M. Wind Power: Turbine Design, Selection, and Optimization. Wiley & Sons, Incorporated, John, 2013.

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28

Lyatkher, Victor M. Wind Power: Turbine Design, Selection, and Optimization. Wiley & Sons, Limited, John, 2013.

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29

Baltes, Boris B., i Cort W. Rudolph. The Theory of Selection, Optimization, and Compensation. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199746521.013.0044.

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30

Optimization Approaches For Solving String Selection Problems. Springer-Verlag New York Inc., 2013.

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31

Lyatkher, Victor. Wind Power: Turbine Design, Selection, and Optimization. Wiley & Sons, Incorporated, John, 2013.

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32

Pardalos, P. M. Optimization Approaches for Solving String Selection Problems. Springer London, Limited, 2013.

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33

Lyatkher, Victor M. Wind Power: Turbine Design, Selection, and Optimization. Wiley & Sons, Incorporated, John, 2013.

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34

Lyatkher, Victor M. Wind Power: Turbine Design, Selection, and Optimization. Wiley & Sons, Incorporated, John, 2013.

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35

Puhle, Michael. Bond Portfolio Optimization. Springer London, Limited, 2008.

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36

Bond Portfolio Optimization. Springer, 2008.

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37

Basile, Angelo, i Fausto Gallucci. Membranes for Membrane Reactors: Preparation, Optimization and Selection. Wiley & Sons, Limited, John, 2011.

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38

Yu, Jie, Yi Wang, Maosheng Zheng, Haipeng Teng i Ying Cui. Probability-Based Multi-Objective Optimization for Material Selection. Springer, 2022.

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39

Basile, Angelo, i Fausto Gallucci. Membranes for Membrane Reactors: Preparation, Optimization and Selection. Wiley & Sons, Incorporated, John, 2010.

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40

Basile, Angelo, i Fausto Gallucci. Membranes for Membrane Reactors: Preparation, Optimization and Selection. Wiley & Sons, Incorporated, John, 2011.

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41

Basile, Angelo, i Fausto Gallucci. Membranes for Membrane Reactors: Preparation, Optimization and Selection. Wiley & Sons, Incorporated, John, 2010.

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42

Route, Aircraft Prioritization and Selection for Airlift Mobility Optimization. Storming Media, 1996.

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43

Cui, Zhihua. Social Emotional Optimization Algorithm with Random Emotional Selection Strategy. INTECH Open Access Publisher, 2012.

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44

Handen, Jeffrey S. Industrialization of Drug Discovery: From Target Selection Through Lead Optimization. Taylor & Francis Group, 2005.

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45

Optimization of United States Marine Corps Officer Career Path Selection. Storming Media, 2000.

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46

Industrialization of drug discovery: From target selection through lead optimization. Boca Raton, FL: Taylor & Francis/CRC Press, 2005.

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47

Handen, Jeffrey S. Industrialization of Drug Discovery: From Target Selection Through Lead Optimization. Taylor & Francis Group, 2005.

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48

Mekarapiruk, Wichaya. Simultaneous optimal parameter selection and dynamic optimization using iterative dynamic programming. 2001.

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49

Parthasarathy, T. Selection Theorems and Their Applications. Springer London, Limited, 2006.

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50

Travers, Frank J. Investment Manager Analysis: A Comprehensive Guide to Portfolio Selection, Monitoring and Optimization. Wiley & Sons, Incorporated, John, 2011.

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