Advanced Techniques in Optimization for Machine Learning and Imaging, Benfenati A., Porta F., Bubba T.A., 2024

Advanced Techniques in Optimization for Machine Learning and Imaging, Benfenati A., Porta F., Bubba T.A., 2024.
    
   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.




Специализация-минимизация для разреженных SVM.
Support Vector Machines (SVMs) are well-tailored for regression and classification applications. They were introduced in the seminal work [15] for supervised learning. In addition to being grounded on sound optimization techniques [28, 34], various extensions of them can be performed. They remain one of the most widely used methods in classification tasks despite the increasing role played by neural networks. As linear classifiers, SVMs have been shown to outperform many supervised methods [9, 23, 26]. Real-world applications include image classification [25], face detection [33,37], hand-written character recognition [21], melanoma classification [1, 2], text categorization [12, 27]. The interested reader can find a complete review in [10].

The supervised learning problem in the SVM framework consists in minimizing a suitable function measuring the distance between the predicted and the true labels corresponding to a dataset sample. This minimization is carried out with respect to the SVM model parameters. The SVM training problem may be formulated as a quadratic programming one [36]. It may involve least squares loss (hard-margin SVM) under a suitable constraint [32], or the hinge loss [12].

Contents.
Cover.
Front Matter.
STEMPO—Dynamic X-Ray Tomography Phantom.
On a Fixed-Point Continuation Method for a Convex Optimization Problem Majoration-Minimization for Sparse SVMs.
Bilevel Learning of Regularization Models and Their Discretization for Image Deblurring and Super-Resolution.
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms.
On the Inexact Proximal Gauss-Newton Methods for Regularized Nonlinear Least Squares Problems.



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