Research on a Least Squares Thresholding Algorithm for Pavement Crack Detection

非线性最小二乘教程

通过移动的最小二乘法改变和自定义的控制点操作图片。Image Deformation Using Moving Least Squares 移动最小二乘法 图像变形（matlab实现）

Image Deformation Using Moving Least Squares 算法的matlab实现。通过移动的最小二乘法改变和自定义的控制点操作图片。

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This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.