机器学习和影像学中的高级优化技术

English | 2024 | ISBN: 978-981-97-6769-4 | 173 pages | True EPUB PDF | 58 MB

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.

近年来，非线性优化在机器学习与成像技术的交叉领域的发展中发挥了关键作用。目前这本书汇集了该领域的最新研究成果，无论是重新审视已巩固的思想来提供形式化的理论保证，还是为成像中的挑战性逆向问题进行比较数值研究。这些论文的研究成果源自于2022年6月在意大利罗马举行的INdAM研讨会“优化技术在机器学习和成像中高级技术”，该研讨会的主题是优化技术。 所涵盖的领域包括基于驱动模型的变分正则化非光滑优化技术、用于线性逆向问题选择正则化参数的选择策略固定点延续算法及其理论分析。通过极大化-极小化方法训练的支持向量机的不同视角，以及将贝叶斯统计框架推广到成像问题，还包括创建基准数据集以测试新的方法和算法。

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