Add to ORKGThe performance of support vector machines in non-linearly-separable classification problems strongly relies on the kernel function. Towards an automatic machine learning approach for this technique, many research outputs have been produced dealing with the challenge of automatic learn- ing of good-performing kernels for support vector machines. However, these works have been carried out without a thorough analysis of the set of components that influence the behavior of support vector machines and their interaction with the kernel. These components are related in an in- tricate way and it is difficult to provide a comprehensible analysis of their joint effect. In this paper we try to fill this gap introducing the necessary steps in order to...
We briefly describe the main ideas of statistical learning theory, support vector machines, and kern...
The problem of combining different sources of information arises in several situations, for instance...
Abstract. The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the ...
In recent years Support Vector Machines (SVM) have gained increasing popularity over other classific...
It is the most critical for finding the best kernel to apply the kernel-based algorithms in practice...
In the 1990s, a new type of learning algorithm was developed, based on results from statistical lear...
SVMÉcole thématiqueKernel Machines is a term covering a large class of learning algorithms, includin...
Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods...
Kernel methods, a new generation of learning algorithms, utilize techniques from optimization, stati...
The use of Multiple Kernel Learning (MKL) for Support Vector Machines (SVM) in Machine Learning task...
Support Vector Machines have been used to do classification and regression analysis. One important ...
Recently, training support vector machines with indef-inite kernels has attracted great attention in...
Kernel Learning is widely used in pattern recognition and classification problems. We look at the be...
Support Vector (SV) Machines combine several techniques from statistics, machine learning and neural...
Artificial intelligence (AI) and machine learning (ML) have influenced every part of our day-to-day ...
We briefly describe the main ideas of statistical learning theory, support vector machines, and kern...
The problem of combining different sources of information arises in several situations, for instance...
Abstract. The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the ...
In recent years Support Vector Machines (SVM) have gained increasing popularity over other classific...
It is the most critical for finding the best kernel to apply the kernel-based algorithms in practice...
In the 1990s, a new type of learning algorithm was developed, based on results from statistical lear...
SVMÉcole thématiqueKernel Machines is a term covering a large class of learning algorithms, includin...
Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods...
Kernel methods, a new generation of learning algorithms, utilize techniques from optimization, stati...
The use of Multiple Kernel Learning (MKL) for Support Vector Machines (SVM) in Machine Learning task...
Support Vector Machines have been used to do classification and regression analysis. One important ...
Recently, training support vector machines with indef-inite kernels has attracted great attention in...
Kernel Learning is widely used in pattern recognition and classification problems. We look at the be...
Support Vector (SV) Machines combine several techniques from statistics, machine learning and neural...
Artificial intelligence (AI) and machine learning (ML) have influenced every part of our day-to-day ...
We briefly describe the main ideas of statistical learning theory, support vector machines, and kern...
The problem of combining different sources of information arises in several situations, for instance...
Abstract. The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the ...