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Advanced Techniques in Optimization for Machine Learning and Imaging

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 theo...

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Gorde:
Xehetasun bibliografikoak
Erakunde egilea: SpringerLink (Online service)
Beste egile batzuk: Benfenati, Alessandro (Argitaratzailea), Porta, Federica (Argitaratzailea), Bubba, Tatiana Alessandra (Argitaratzailea), Viola, Marco (Argitaratzailea)
Formatua: Baliabide elektronikoa eBook
Hizkuntza:ingelesa
Argitaratua: Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Edizioa:1st ed. 2024.
Saila:Springer INdAM Series, 61
Gaiak:
Machine learning.
Mathematical optimization.
Mathematical analysis.
Machine Learning.
Optimization.
Analysis.
Sarrera elektronikoa:https://doi.org/10.1007/978-981-97-6769-4
Etiketak: Etiketa erantsi
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
Aurkibidea:
  • 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.

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