03876nam a22005775i 4500 978-981-97-6769-4 DE-He213 20251030113124.0 cr nn 008mamaa 241002s2024 si | s |||| 0|eng d 9789819767694 978-981-97-6769-4 10.1007/978-981-97-6769-4 doi Q325.5-.7 UYQM bicssc MAT029000 bisacsh UYQM thema Advanced Techniques in Optimization for Machine Learning and Imaging [electronic resource] / edited by Alessandro Benfenati, Federica Porta, Tatiana Alessandra Bubba, Marco Viola. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. X, 165 p. 81 illus., 57 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer INdAM Series, 2281-5198 ; 61 1.STEMPO dynamic Xray tomography phantom -- 2.On a fixed point continuation method for a convex optimization problem -- 3.Majoration Minimization for Sparse SVMs -- 4.Bilevel learning of regularization models and their discretization for image deblurring and super resolution -- 5.Non Log Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms -- 6.On the inexact proximal Gauss-Newton methods for regularized nonlinear least squares problems. In recent years, non-linear optimization has had a crucial role in the development of modern techniques at the interface of machine learning and imaging. The present book is a collection of recent contributions in the field of optimization, either revisiting consolidated ideas to provide formal theoretical guarantees or providing comparative numerical studies for challenging inverse problems in imaging. The work of these papers originated in the INdAM Workshop “Advanced Techniques in Optimization for Machine learning and Imaging” held in Roma, Italy, on June 20-24, 2022. The covered topics include non-smooth optimisation techniques for model-driven variational regularization, fixed-point continuation algorithms and their theoretical analysis for selection strategies of the regularization parameter for linear inverse problems in imaging, different perspectives on Support Vector Machines trained via Majorization-Minimization methods, generalization of Bayesian statistical frameworks to imaging problems, and creation of benchmark datasets for testing new methods and algorithms. Machine learning. 55059 Mathematical optimization. 65522 Mathematical analysis. 64178 Machine Learning. 55059 Optimization. 65528 Analysis. Benfenati, Alessandro. editor. edt http://id.loc.gov/vocabulary/relators/edt 85520 Porta, Federica. editor. edt http://id.loc.gov/vocabulary/relators/edt 85521 Bubba, Tatiana Alessandra. editor. edt http://id.loc.gov/vocabulary/relators/edt 85522 Viola, Marco. editor. edt http://id.loc.gov/vocabulary/relators/edt 85523 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819767687 Printed edition: 9789819767700 Printed edition: 9789819767717 Springer INdAM Series, 2281-5198 ; 61 77740 https://doi.org/10.1007/978-981-97-6769-4 ZDB-2-SMA ZDB-2-SXMS 63956 63956