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http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=928828
This new approach lends to solving convex optimization problems and also the model complexity follows from this solution. We especially focus on a least squares support vector machine formulation (LS-SVM) which enables to solve highly nonlinear and noisy black-box modelling problems, even in very high dimensional input spaces.
https://www.sciencedirect.com/science/article/pii/S0947358001711521
We discuss a method of least squares support vector machines (LS-SVM), which has been extended to recurrent models and use in optimal control problems. We explain how robust nonlinear estimation and sparse approximation can be done by means of this kernel based technique. A short overview of hyperparameter tuning methods is given.Cited by: 203
https://www.researchgate.net/publication/3899462_Nonlinear_modelling_and_support_vector_machines
We especially focus on a least squares support vector machine formulation (LS-SVM) which enables to solve highly nonlinear and noisy black-box modelling problems, even in very high dimensional ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.7.3646
This new approach leads to solving convex optimization problems and also the model complexity follows from this solution. We especially focus on a least squares support vector machine formulation (LS-SVM) which enables to solve highly nonlinear and noisy black-box modelling problems, even in very high dimensional input spaces.
https://link.springer.com/10.1007/11753728_66
Abstract. This paper firstly provides an short introduction to least square support vector machine (LSSVM), a new class of kernel-based techniques introduced in statistical learning theory and structural risk minimization, then designs a training algorithm for LSSVM, and uses LSSVM to model and control nonlinear systems.Cited by: 2
https://www.researchgate.net/publication/245441278_Support_Vector_Machines_A_Nonlinear_Modelling_and_Control_Perspective
Support Vector Machines: A Nonlinear Modelling and Control Perspective Article (PDF Available) in European Journal of Control 7(2-3):311-327 · December 2001 with 825 Reads How we measure 'reads'
https://link.springer.com/article/10.1007/s10288-018-0378-2
May 23, 2018 · Support Vector Machine (SVM) is one of the most important class of machine learning models and algorithms, and has been successfully applied in …Cited by: 3
http://people.ee.duke.edu/~lcarin/svm_nips2014.pdf
Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling Ricardo Henao, Xin Yuan and Lawrence Carin Department of Electrical and Computer Engineering Duke University, Durham, NC 27708 fr.henao,xin.yuan,[email protected] Abstract A new Bayesian formulation is developed for nonlinear support vector machines
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