Name: Advanced Techniques in Optimization for Machine Learning and Imaging
SKU: 64792754
Price: 220.41 USD
Availability: InStock
Author: Alessandro Benfenati;Tatiana Alessandra Bubba;Marco Viola;Federica Porta

portada Advanced Techniques in Optimization for Machine Learning and Imaging

Advanced Techniques in Optimization for Machine Learning and Imaging

Formato

Libro Físico

Editorial

Springer Nature

Autor

Alessandro Benfenati;Tatiana Alessandra Bubba;Marco Viola;Federica Porta

Colección

Springer INdAM Series

Año

2024

N° páginas

165

Encuadernación

Tapa Dura

Dimensiones

23.5x15.5 cm

ISBN13

9789819767687

Advanced Techniques in Optimization for Machine Learning and Imaging

Alessandro Benfenati;Tatiana Alessandra Bubba;Marco Viola;Federica Porta (Autor) · Springer Nature · Tapa Dura

Libro Nuevo Importado

Envío: 20 a 27 días háb.

$ 400.74$ 220.41

-45%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan más de 100 unidades

$ 220.41

Llega entre el 23 Sep y el 06 Oct a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "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 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.