Add to ORKGIn this work a new methodology for automatic selection of the free parameters in the Least Squares–Support Vector Machines (LS-SVM) regression oriented algorithm is proposed. We employ a multidimensional Generalized Cross-Validation analysis in the linear equation system of LS-SVM. Our approach does not require a prior knowledge about the influence of the LS-SVM free parameters in the results. The methodology is tested on two artificial and two real-world data sets. According to the results our methodology computes suitable regressions with competitive relative errors
In this paper, we propose a method to select support vectors to improve the performance of support v...
To overcome the disadvantage of CV-ACC method that the high-density sample region may be close to th...
In this paper, we propose a method to select support vectors to improve the performance of support v...
RESUMEN: En este trabajo, se propone una metodología para la selección automática de los parámetros ...
While the model parameters of many kernel learning methods are given by the solution of a convex opt...
While the model parameters of many kernel learning methods are given by the solution of a convex opt...
Among neural models the Support Vector Machine (SVM) solutions are attracting increasing attention, ...
Among neural models the Support Vector Machine (SVM) solutions are attracting increasing attention, ...
In this paper, we propose a method to select support vectors to improve the performance of support v...
Support Vector Machine has appeared as an active study in machine learning community and extensively...
We propose a fast, incremental algorithm for designing linear regression models. The proposed algori...
Nonparametric regression is a very popular tool for data analysis because thesetechniques impose few...
This paper proposes the use of least-squares support vector machines (LS-SVMs) as a relatively new n...
We propose a fast, incremental algorithm for designing linear regression models. The proposed algori...
Contains fulltext : 60399.pdf (publisher's version ) (Open Access)This paper propo...
In this paper, we propose a method to select support vectors to improve the performance of support v...
To overcome the disadvantage of CV-ACC method that the high-density sample region may be close to th...
In this paper, we propose a method to select support vectors to improve the performance of support v...
RESUMEN: En este trabajo, se propone una metodología para la selección automática de los parámetros ...
While the model parameters of many kernel learning methods are given by the solution of a convex opt...
While the model parameters of many kernel learning methods are given by the solution of a convex opt...
Among neural models the Support Vector Machine (SVM) solutions are attracting increasing attention, ...
Among neural models the Support Vector Machine (SVM) solutions are attracting increasing attention, ...
In this paper, we propose a method to select support vectors to improve the performance of support v...
Support Vector Machine has appeared as an active study in machine learning community and extensively...
We propose a fast, incremental algorithm for designing linear regression models. The proposed algori...
Nonparametric regression is a very popular tool for data analysis because thesetechniques impose few...
This paper proposes the use of least-squares support vector machines (LS-SVMs) as a relatively new n...
We propose a fast, incremental algorithm for designing linear regression models. The proposed algori...
Contains fulltext : 60399.pdf (publisher's version ) (Open Access)This paper propo...
In this paper, we propose a method to select support vectors to improve the performance of support v...
To overcome the disadvantage of CV-ACC method that the high-density sample region may be close to th...
In this paper, we propose a method to select support vectors to improve the performance of support v...