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• W2180026118 abstract "Kinetic models are being increasingly used as a systematic framework to understand function in biological systems. Calibration of these nonlinear dynamic models remains challenging due to the nonconvexity and ill-conditioning of the associated inverse problems. Nonconvexity can be dealt with suitable global optimization. Here, we focus on simultaneously dealing with ill-conditioning by making use of proper regularization methods. Regularized calibrations ensure the best trade-offs between bias and variance, thus reducing over-fitting. We present a critical comparison of several methods, and guidelines for properly tuning them. The performance of this procedure and its advantages are illustrated with a well known benchmark problem considering several scenarios of data availability and measurement noise." @default.
• W2180026118 created "2016-06-24" @default.
• W2180026118 creator A5050239570 @default.
• W2180026118 creator A5090264675 @default.
• W2180026118 date "2014-01-01" @default.
• W2180026118 modified "2023-09-26" @default.
• W2180026118 title "Improved Parameter Estimation in Kinetic Models: Selection and Tuning of Regularization Methods" @default.
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• W2180026118 doi "https://doi.org/10.1007/978-3-319-12982-2_4" @default.
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