We use least squares support vector machine (LS-SVM) utilizing a binary decision tree for classification of cardiotocogram to determine the fetal state. The parameters of LS-SVM are optimized by particle swarm optimization. The robustness of the method is examined by running 10-fold cross-validation. The performance of the method is evaluated in terms of overall classification accuracy. Additio...

This article presents how to use the least-squares (LS) regression method to price the American options on basis of the algorithm in a paper by Clement, Lamberton & Protter[1]. The key to LS is the approximation of the conditional expectation functions which determine the optimal exercise strategy. In this paper, through the detailed description of the algorithm and presentation of convergence,...

Let T be a tree with vertex set V (T ) = {1, . . . , n} and with a positive weight associated with each edge. The tree distance between i and j is the weight of the ij-path. Given a symmetric, positive real valued function on V (T )×V (T ), we consider the problem of approximating it by a tree distance corresponding to T, by the least-squares method. The problem is solved explicitly when T is a...

A windowed Fourier method is proposed for approximation of nonperiodic functions on equispaced nodes. Spectral convergence is obtained in most of the domain, except near the boundaries, where polynomial least-squares is used to correct the approximation. Because the method can be implemented using partition of unit and domain decomposition, it is suitable for adaptive and parallel implementatio...

The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM), combined with its sparsified version (sparse online LS-OC-SVM). LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regul...

The polynomial chaos (PC) method has been widely adopted as a computationally feasible approach for uncertainty quantification (UQ). Most studies to date have focused on non-stiff systems. When stiff systems are considered, implicit numerical integration requires the solution of a nonlinear system of equations at every time step. Using the Galerkin approach the size of the system state increase...

the combinations of inductively coupled plasma-optical emission spectrometry (icp-oes) and three classification algorithms, i.e., partial least squares discriminant analysis (pls-da), least squares support vector machine (ls-svm) and soft independent modeling of class analogies (simca), for discriminating different brands of iranian bottled mineral waters, were explored. icp-oes was used for th...

A regularized Newton-like method for solving nonnegative least-squares problems is proposed and analysed in this paper. A preconditioner for KKT systems arising in the method is introduced and spectral properties of the preconditioned matrix are analysed. A bound on the condition number of the preconditioned matrix is provided. The bound does not depend on the interior-point scaling matrix. Pre...

Extreme Support Vector Machine (ESVM) is a nonlinear robust SVM algorithm based on regularized least squares optimization for binary-class classification. In this paper, a novel algorithm for regression tasks, Extreme Support Vector Regression (ESVR), is proposed based on ESVM. Moreover, kernel ESVR is suggested as well. Experiments show that, ESVR has a better generalization than some other tr...