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Advanced techniques in optimization ...

Benfenati, Alessandro.

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Advanced techniques in optimization for machine learning and imaging

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advanced techniques in optimization for machine learning and imaging/ edited by Alessandro Benfenati ... [et al.].
其他作者:	Benfenati, Alessandro.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	x, 165 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Mathematical optimization. -
電子資源:	https://doi.org/10.1007/978-981-97-6769-4
ISBN:	9789819767694

Advanced techniques in optimization for machine learning and imaging
Advanced techniques in optimization for machine learning and imaging[electronic resource] /edited by Alessandro Benfenati ... [et al.]. - Singapore :Springer Nature Singapore :2024. - x, 165 p. :ill. (some col.), digital ;24 cm. - Springer INdAM series,v. 612281-5198 ;. - Springer INdAM series ;v. 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.

ISBN: 9789819767694

Standard No.: 10.1007/978-981-97-6769-4doiSubjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Advanced techniques in optimization for machine learning and imaging
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520 $a 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.
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