论文标题
使用监督的学习来构建最佳正规化术语和最佳的多分析分析
Using Supervised Learning to Construct the Best Regularization Term and the Best Multiresolution Analysis
论文作者
论文摘要
通过计算机技术的最新进展，导致更强大的处理器的发明，使用数据培训创建模型的重要性比以往任何时候都更大。鉴于此问题的重要性，这项工作试图在机器学习，反问题和应用谐波分析之间建立联系。受到[12、17、22、30]中引入的方法的启发，该方法是小波和反问题之间的连接，我们提供了一个模型，具有在信号处理中的应用方面学习的能力。为了达到此模型，必须面对双层优化问题。为了解决此问题，提出了一系列步骤函数，即将证明其收敛性。这些步骤函数中的每一个都来自$ \ mathbb {r^n} $的几个约束优化问题，这些问题将在此处介绍。
By the recent advances in computer technology leading to the invention of more powerful processors, the importance of creating models using data training is even greater than ever. Given the significance of this issue, this work tries to establish a connection among Machine Learning, Inverse Problems, and Applied Harmonic Analysis. Inspired by methods introduced in [12, 17, 22, 30], which are connections between Wavelet and Inverse Problems, we offer a model with the capability of learning in terms of an application in signal processing. In order to reach this model, a bi-level optimization problem will have to be faced. For solving this, a sequence of step functions is presented that its convergence to the solution will be proved. Each of these step functions derives from several constrained optimization problems on $\mathbb{R^n}$ that will be introduced here.
