國立高雄大學圖資館 |

Language: English

Back

Advanced techniques in optimization for machine learning and imaging

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advanced techniques in optimization for machine learning and imagingedited by Alessandro Benfenati ... [et al.].
other author:	Benfenati, Alessandro.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	x, 165 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Mathematical optimization.
Online resource:	https://doi.org/10.1007/978-981-97-6769-4
ISBN:	9789819767694$q(electronic bk.)

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.6..

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$q(electronic bk.)

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

183292
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Advanced techniques in optimization for machine learning and imaging
LDR:02634nmm a2200337 a 4500 001 669607
003 DE-He213
005 20241002130240.0
006 m d
007 cr nn 008maaau
008 250120s2024 si s 0 eng d
020 $a 9789819767694$q(electronic bk.)
020 $a 9789819767687$q(paper)
024 7 $a 10.1007/978-981-97-6769-4 $2 doi
035 $a 978-981-97-6769-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402.5
072 7 $a UYQM $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 519.6 $2 23
090 $a QA402.5 $b .A244 2024
245 0 0 $a Advanced techniques in optimization for machine learning and imaging $h [electronic resource] / $c edited by Alessandro Benfenati ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a x, 165 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer INdAM series, $x 2281-5198 ; $v v. 61
505 0 $a 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.
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.
650 0 $a Mathematical optimization. $3 183292
650 0 $a Nonlinear theories. $3 182014
650 0 $a Machine learning $x Mathematics. $3 857106
650 0 $a Image processing $x Digital techniques $x Mathematics. $3 256957
650 1 4 $a Machine Learning. $3 833608
650 2 4 $a Optimization. $3 274084
650 2 4 $a Analysis. $3 273775
700 1 $a Benfenati, Alessandro. $3 982590
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Springer INdAM series ; $v v.6. $3 679907
856 4 0 $u https://doi.org/10.1007/978-981-97-6769-4
950 $a Mathematics and Statistics (SpringerNature-11649)