Practical applications call for efficient model selection criteria for multiclass support vector machine (SVM) classification. To solve this problem, this paper develops two model selection criteria by combining or redefining the radius-margin bound use
A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing...
We address the problem of model selection for Support Vector Machine (SVM) classification. For fixed...
A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing...
Practical applications call for efficient model selection criteria for multiclass support vector mac...
Practical applications call for efficient model selection criteria for multiclass support vector mac...
Abstract. In this article, model selection for support vector machines is viewed as a multi-objectiv...
In this chapter, we revise several methods for SVM model selection, deriving from different approach...
New functionals for parameter (model) selection of Support Vector Machines are introduced based on t...
In recent years, Support Vector Machines (SVM) have been extensively applied to deal with various da...
http://asmda2005.enst-bretagne.fr/International audienceIn the framework of statistical learning, fi...
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support...
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support...
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support...
Abstract: Firstly, a distinguishable condition is proposed for separating the features by linear cl...
A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing...
A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing...
We address the problem of model selection for Support Vector Machine (SVM) classification. For fixed...
A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing...
Practical applications call for efficient model selection criteria for multiclass support vector mac...
Practical applications call for efficient model selection criteria for multiclass support vector mac...
Abstract. In this article, model selection for support vector machines is viewed as a multi-objectiv...
In this chapter, we revise several methods for SVM model selection, deriving from different approach...
New functionals for parameter (model) selection of Support Vector Machines are introduced based on t...
In recent years, Support Vector Machines (SVM) have been extensively applied to deal with various da...
http://asmda2005.enst-bretagne.fr/International audienceIn the framework of statistical learning, fi...
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support...
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support...
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support...
Abstract: Firstly, a distinguishable condition is proposed for separating the features by linear cl...
A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing...
A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing...
We address the problem of model selection for Support Vector Machine (SVM) classification. For fixed...
A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing...