io-port 06474690 Laurain, Vincent; Tóth, Roland; Piga, Dario; Zheng, Wei Xing An instrumental least squares support vector machine for nonlinear system identification. Automatica 54, Article ID 6308, 340-347 (2015). Summary: Least-Squares Support Vector Machines (LS-SVMs), originating from Statistical Learning and Reproducing Kernel Hilbert Space (RKHS) theories, represent a promising approach ...

a quantitative structure-activity relationship (qsar) study was conducted for the prediction of inhibitory activity of 1-phenyl[2h]-tetrahydro-triazine-3-one analogues as inhibitors of 5-lipoxygenase. the inhibitory activities of the 1-phenyl[2h]-tetrahydro-triazine-3-one analogues modeled as a function of molecular structures using chemometrics methods such as multiple linear regression (mlr) ...

this paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (lssvm). two wavelet selection criteria maximum energy to shannon entropy ratio and maximum relative wavelet energy are used and compared to select an appropriate wavelet for feature extraction. the fault diagnosis method 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...

Support Vector Machines is a powerful methodology for solving problems in nonlinear classification, function estimation and density estimation which has also led recently to many new developments in kernel based learning in general. In these methods one solves convex optimization problems, typically quadratic programs. We focus on Least Squares Support Vector Machines which are reformulations t...

In this paper, we introduce a new technique for the separation of physical and spurious modes based on an initial clustering in frequency-damping space, followed by a self-learning classification algorithm. For the classification, Least Squares Support Vector Machines are used, a Least Squares version of the theory of Support Vector Machines which maps the classification problem to a high-dimen...

In this paper we study nonlinear ARX models in relation to a class of kernel based models which make use of kernel induced feature spaces, a methodology which is common in the area of support vector machines (SVMs). Methods are proposed for extending the use of least squares support vector machine (LS-SVM) models towards a robust setting. In order to understand the robustness of these estimator...

This paper presents a new method for the selection of the two hyperparameters of Least Squares Support Vector Machine (LS-SVM) approximators with Gaussian Kernels. The two hyperparameters are the width σ of the Gaussian kernels and the regularization parameter λ. For different values of σ, a Nonparametric Noise Estimator (NNE) is introduced to estimate the variance of the noise on the outputs. ...

This paper presents a multiclass, multilabel implementation of Least Squares Support Vector Machines (LS-SVM) for direction of arrival (DOA) estimation in a CDMA system. For any estimation or classification system the algorithm’s capabilities and performance must be evaluated. Specifically, for classification algorithms a high confidence level must exist along with a technique to automatically ...