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

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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

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埋葬

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

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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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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. Özmen, 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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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. Özmen, G.-W. Weber and A. Karimov, A ne

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