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Titlebook: Robust Optimization of Spline Models and Complex Regulatory Networks; Theory, Methods and Ayse ?zmen Book 2016 Springer International Publ

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發(fā)表于 2025-3-21 18:29:20 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Robust Optimization of Spline Models and Complex Regulatory Networks
副標題Theory, Methods and
編輯Ayse ?zmen
視頻videohttp://file.papertrans.cn/832/831342/831342.mp4
概述new methods of robust optimization to handle uncertainty and non-linearity in.complex regulatory networks.Providesguidance in the trade-off between accuracy and robustness.Exemplifiesthe new methods i
叢書名稱Contributions to Management Science
圖書封面Titlebook: Robust Optimization of Spline Models and Complex Regulatory Networks; Theory, Methods and  Ayse ?zmen Book 2016 Springer International Publ
描述This book introduces methods of robust optimization in multivariateadaptive regression splines (MARS) and Conic MARS in order to handleuncertainty and non-linearity. The proposed techniques are implemented andexplained in two-model regulatory systems that can be found in the financialsector and in the contexts of banking, environmental protection, system biologyand medicine. The book provides necessarybackground information on multi-model regulatory networks, optimizationand regression. It presents the theory of and approaches to robust (conic)multivariate adaptive regression splines - R(C)MARS – and robust (conic)generalized partial linear models – R(C)GPLM – under polyhedral uncertainty. Further,it introduces spline regression models for multi-model regulatory networks andinterprets (C)MARS results based on different datasets for the implementation.It explains robust optimization in these models in terms of both the theory andmethodology. In this context it studies R(C)MARS results with differentuncertainty scenarios for a numerical example. Lastly, the book demonstratesthe implementation of the method in a number of applications from thefinancial, energy, and environmental secto
出版日期Book 2016
關(guān)鍵詞robust conic optimization; conic quadratic programming; complex multi-modal regulatory networks; robust
版次1
doihttps://doi.org/10.1007/978-3-319-30800-5
isbn_softcover978-3-319-80890-1
isbn_ebook978-3-319-30800-5Series ISSN 1431-1941 Series E-ISSN 2197-716X
issn_series 1431-1941
copyrightSpringer International Publishing Switzerland 2016
The information of publication is updating

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沙發(fā)
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1431-1941 between accuracy and robustness.Exemplifiesthe new methods iThis book introduces methods of robust optimization in multivariateadaptive regression splines (MARS) and Conic MARS in order to handleuncertainty and non-linearity. The proposed techniques are implemented andexplained in two-model regulato
地板
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New Robust Analytic Tools,e given data, in both input and output variables, are affected with noise of various kinds, and the scenarios which represent the developments in time, are not deterministic either. Since the global environmental and economic crisis has caused the necessity for an essential restructuring of the appr
6#
發(fā)表于 2025-3-22 15:35:35 | 只看該作者
Spline Regression Models for Complex Multi-Model Regulatory Networks, and we represented and applied our methods to real-world data from different sectors. In this chapter, we apply the data mining tool of regression and classification, (C)MARS, on a dynamics. By this, the amount of condition grows, since each time point (a discrete time, in our case) can be regarded
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發(fā)表于 2025-3-22 21:08:36 | 只看該作者
Robust Optimization in Spline Regression Models for Regulatory Networks Under Polyhedral Uncertaintwe demonstrated the effectiveness of these approaches by a numerical experiment. For that study (A. ?zmen, E. Kropat and G.-W. Weber, Spline Regression Models for Complex Multi-modal Regulatory Networks, Optimization Methods and Software, 29(3), pp.?515–534, 2014), CMARS provides better results than
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發(fā)表于 2025-3-22 23:58:22 | 只看該作者
Real-World Application with Our Robust Tools, achieving an optimal trade-off between risk and return. In this way, robustification is starting to draw more attention in finance; in particular, some studies report promising results using robust statistical techniques in financial markets. In the study (A. ?zmen, G.-W. Weber and A. Karimov, A ne
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