This paper introduces the first adaptive least-squares finite element method (LS-FEM) for the Stokes equationswith optimal convergence rates based on the newest vertex bisection with lowest-order Raviart-Thomas and conforming P1 discrete spaces for the divergence least-squares formulation in 2D. Although the least-squares functional is a reliable and efficient error estimator, the novel refinem...

A least squares fuzzy support vectormachine (LS-FSVM)model that integrates advantages of fuzzy support vectormachine (FSVM) and least squares method is proposed for credit risk evaluation. In the proposed LS-FSVM model, the purpose of incorporating the concepts of fuzzy sets is to add generalization capability and outlier insensitivity, while the least squares method is adopted to reduce the co...

1 1 Basics of Developing Regression Models from Data 3 1.1 Classic Regression Support Vector Machines Learning Setting 3 2 Active Set Method for Solving QP Based SVMs’ Learning 11 3 Active Set Least Squares (AS-LS) Regression 15 3.1 Implementation of the Active Set Least Squares Algorithm 19 3.1.1 Basics of Orthogonal Transformation 20 3.1.2 An Iterative Update of the QR Decomposition by Househ...

1 1 Basics of Developing Regression Models from Data 3 1.1 Classic Regression Support Vector Machines Learning Setting 3 2 Active Set Method for Solving QP Based SVMs’ Learning 11 3 Active Set Least Squares (AS-LS) Regression 15 3.1 Implementation of the Active Set Least Squares Algorithm 19 3.1.1 Basics of Orthogonal Transformation 20 3.1.2 An Iterative Update of the QR Decomposition by Househ...

Most of the current theory for dynamic programming algorithms focuses on finite state, finite action Markov decision problems, with a paucity of theory for the convergence of approximation algorithms with continuous states. In this paper we propose a policy iteration algorithm for infinite-horizon Markov decision problems where the state and action spaces are continuous and the expectation cann...

A scheme of direct adaptive H∞ control based on least squares support vector machines (LS-SVM) is proposed for a class of nonlinear uncertain systems. In this method, LS-SVM is employed to construct the adaptive controller, and an on-line learning rule for the weighting vector and bias is derived. A parameter selection method based on the genetic algorithm (GA) is given for LS-SVM regression wi...

We propose a sequential method to estimate monotone convex function that consists of: (i) monotone regression via solving a constrained least square problem and (ii) convexification of the monotone regression estimate via solving an associated constrained uniform approximation problem. We show that this method is faster than the constrained least squares (LS) method. The ratio of computation ti...