Add to ORKGIn this document we propose the use of a widely known learning-from-examples paradigm, namely the Support Vector Machines for Regression (SVRs), for system identification problems. We start off with the identification of a simple linear system taken from the literature, and proceed with the non-linear case as a second step
This open access book provides a comprehensive treatment of recent developments in kernel-based iden...
Abstract-In this work we discuss the application of Support Vector Machines to the problem of identi...
Abstract—Nonlinear system identification based on support vector machines (SVM) has been usually add...
In this work we deal with the application of Support Vector Machines for Regression (SVRs) to the pr...
Abstract: Support Vector Machines (SVM) have become a subject of intensive study in sta-tistical lea...
In this paper, a system identification method for linear regression models based on support vector m...
In this paper, we demonstrate the use of support vector regression (SVR) techniques for black-box sy...
In this paper, we demonstrate the use of support vector regression (SVR) techniques for black-box sy...
Abstract—As an emerging non-parametric modeling technique, the methodology of support vector regress...
In this report we present an introductory overview of Support Vector Machines (SVMs). SVMs are super...
This paper presents a new approach to auto-regressive and moving average (ARMA) modeling based on th...
Least-Squares Support Vector Machines (LS-SVMs), originating from Statistical Learning and Reproduci...
Least-Squares Support Vector Machines (LS-SVMs), originating from Statistical Learning and Reproduci...
Least-Squares Support Vector Machines (LS-SVMs), originating from Statistical Learning and Reproduci...
Abstract—In this paper, a nonlinear system identification based on support vector machines (SVM) has...
This open access book provides a comprehensive treatment of recent developments in kernel-based iden...
Abstract-In this work we discuss the application of Support Vector Machines to the problem of identi...
Abstract—Nonlinear system identification based on support vector machines (SVM) has been usually add...
In this work we deal with the application of Support Vector Machines for Regression (SVRs) to the pr...
Abstract: Support Vector Machines (SVM) have become a subject of intensive study in sta-tistical lea...
In this paper, a system identification method for linear regression models based on support vector m...
In this paper, we demonstrate the use of support vector regression (SVR) techniques for black-box sy...
In this paper, we demonstrate the use of support vector regression (SVR) techniques for black-box sy...
Abstract—As an emerging non-parametric modeling technique, the methodology of support vector regress...
In this report we present an introductory overview of Support Vector Machines (SVMs). SVMs are super...
This paper presents a new approach to auto-regressive and moving average (ARMA) modeling based on th...
Least-Squares Support Vector Machines (LS-SVMs), originating from Statistical Learning and Reproduci...
Least-Squares Support Vector Machines (LS-SVMs), originating from Statistical Learning and Reproduci...
Least-Squares Support Vector Machines (LS-SVMs), originating from Statistical Learning and Reproduci...
Abstract—In this paper, a nonlinear system identification based on support vector machines (SVM) has...
This open access book provides a comprehensive treatment of recent developments in kernel-based iden...
Abstract-In this work we discuss the application of Support Vector Machines to the problem of identi...
Abstract—Nonlinear system identification based on support vector machines (SVM) has been usually add...