Add to ORKGAbstract: Support Vector Machines (SVM) have become a subject of intensive study in sta-tistical learning theory. They have been applied to successfully to classification problems and recently extended to regression problems. Support vector machines for regression problems is called Support Vector Regression (SVR). In this paper, a brief introduction to SVR is presented and then a new system identification method based on SVR is proposed for linear in parameter models. The effectiveness of the proposed method is examined through numerical examples
Abstract-In this work we discuss the application of Support Vector Machines to the problem of identi...
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...
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...
In this work we deal with the application of Support Vector Machines for Regression (SVRs) to the pr...
In this document we propose the use of a widely known learning-from-examples paradigm, namely the Su...
Abstract—As an emerging non-parametric modeling technique, the methodology of support vector regress...
Abstract—In this paper, a nonlinear system identification based on support vector machines (SVM) has...
From the beginning, machine learning methodology, which is the origin of artificial intelligence, ha...
The identification of non-linear systems using only observed finite datasets has become a mature res...
The identification of non-linear systems using only observed finite datasets has become a mature res...
Abstract. This paper deals with the application of the Support Vector Method (SVM) methodology to th...
Least-Squares Support Vector Machines (LS-SVMs), originating from Statistical Learning and Reproduci...
Abstract-In this work we discuss the application of Support Vector Machines to the problem of identi...
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...
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...
In this work we deal with the application of Support Vector Machines for Regression (SVRs) to the pr...
In this document we propose the use of a widely known learning-from-examples paradigm, namely the Su...
Abstract—As an emerging non-parametric modeling technique, the methodology of support vector regress...
Abstract—In this paper, a nonlinear system identification based on support vector machines (SVM) has...
From the beginning, machine learning methodology, which is the origin of artificial intelligence, ha...
The identification of non-linear systems using only observed finite datasets has become a mature res...
The identification of non-linear systems using only observed finite datasets has become a mature res...
Abstract. This paper deals with the application of the Support Vector Method (SVM) methodology to th...
Least-Squares Support Vector Machines (LS-SVMs), originating from Statistical Learning and Reproduci...
Abstract-In this work we discuss the application of Support Vector Machines to the problem of identi...
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...