Among neural models the Support Vector Machine (SVM) solutions are attracting increasing attention, mostly because they eliminate certain crucial questions involved by neural network construction. The main drawback of standard SVM is its high computational complexity, therefore recently a new technique, the Least Squares SVM (LS–SVM) has been introduced. In this paper we present an extended vie...

in this paper a novel hybrid algorithm for harmonics estimation in power systems is proposed. the estimation of the harmonic components is a nonlinear problem due to the nonlinearity of phase of sinusoids in distorted waveforms. most researchers implemented nonlinear methods to extract the harmonic parameters. however, nonlinear methods for amplitude estimation increase time of convergence. hen...

a simple and rapid method for the determination of 137ba isotope abundances in water samples by inductively coupled plasma-optical emission spectrometry (icp-oes) coupled with least-squares support vector machine regression (ls-svm) is reported. by evaluation of emission lines of barium, it was found that the emission line at 493.408 nm provides the best results for the determination of 137ba a...

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...

The uniformly minimum variance unbiased (UMVU), maximum likelihood, percentile (PC), least squares (LS) and weighted least squares (WLS) estimators of the probability density function (pdf) and cumulative distribution function are derived for the generalized Rayleigh distribution. This model can be used quite effectively in modelling strength data and also modeling general lifetime data. It has...

A common task in signal processing is to approximate adequately a signal. It is crucial to understand various methods to this task. In this paper we survey three different approximation methods: least squares support vector machine (LS-SVM), multiresolution signal approximation (MSA), and least squares approximation (LSA) to highlight their mathematical relationship. Based on the theoretical an...

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...

Several computer vision problems lead to linear systems affected by noise. These are commonly solved by least-squares estimators, the most popular being ordinary least squares (LS), total least squares (TLS) and generalized total least squares (GTLS). However, the statistical or structural assumptions of these theoretical estimators are very often violated in practice. Given that their computat...