Add to ORKGIn this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and see why this paradigm is successful for many pattern recognition problems. We then embed evolutionary computation into the most prominent representative of this class of learning methods, namely into Support Vector Machines (SVM). In contrast to former applications of evolutionary algorithms to SVM we do not only optimize the method or kernel parameters
Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on t...
Abstract. The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the ...
We briefly describe the main ideas of statistical learning theory, support vector machines, and kern...
In this paper we embed evolutionary computation into statistical learning theory. First, we outline ...
In this paper we embed evolutionary computation into statistical learning theory. First, we outline...
Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on t...
Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on t...
Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on t...
Statistical learning theory was developed by Vapnik. It is a learning theory based on Vapnik-Chervon...
Recently, training support vector machines with indefinite kernels has attracted great attention in ...
Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on t...
Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on t...
Kernel methods, a new generation of learning algorithms, utilize techniques from optimization, stati...
Recently, training support vector machines with indef-inite kernels has attracted great attention in...
Abstract. Kernel-based learning presents a unified approach to machine learning problems such as cla...
Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on t...
Abstract. The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the ...
We briefly describe the main ideas of statistical learning theory, support vector machines, and kern...
In this paper we embed evolutionary computation into statistical learning theory. First, we outline ...
In this paper we embed evolutionary computation into statistical learning theory. First, we outline...
Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on t...
Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on t...
Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on t...
Statistical learning theory was developed by Vapnik. It is a learning theory based on Vapnik-Chervon...
Recently, training support vector machines with indefinite kernels has attracted great attention in ...
Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on t...
Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on t...
Kernel methods, a new generation of learning algorithms, utilize techniques from optimization, stati...
Recently, training support vector machines with indef-inite kernels has attracted great attention in...
Abstract. Kernel-based learning presents a unified approach to machine learning problems such as cla...
Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on t...
Abstract. The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the ...
We briefly describe the main ideas of statistical learning theory, support vector machines, and kern...