Add to ORKGAbstract. In this paper we first overview the main concepts of Statistical Learning Theory, a framework in which learning from examples can be studied in a principled way. We then briefly discuss well known as well as emerging learning techniques such as Regularization Networks and Support Vector Machines which can be justified in term of the same induction principle. Keywords: VC-dimension, structural risk minimization, regularization networks, support vector machines 1
This article gives a short introduction to the main ideas of statistical learning theory, support ve...
During the past decade there has been an explosion in computation and information tech-nology. With ...
The main goal of this course is to study the generalization ability of a number of popular machine l...
Regularization Networks and Support Vector Machines are techniques for solv-ing certain problems of ...
The foundations of Support Vector Machines (SVM) have been developed by Vapnik and are gaining popul...
In this chapter we introduce basic concepts and ideas of the Support Vector Machines (SVM). In the f...
The paper introduces some generalizations of Vapnik's method of structural risk minimisation (S...
Many recently proposed learning algorithms are clearly inspired by Support Vector Machines. Some of ...
Statistical learning theory provides the theoretical basis for many of today's machine learning algo...
Regularization Networks and Support Vector Machines are techniques for solving certain problems of...
Statistical Learning refers to statistical aspects of automated extraction of regularities (structur...
We briefly describe the main ideas of statistical learning theory, support vector machines, and kern...
The Vicinal Risk Minimization principle establishes a bridge between generative models and methods d...
We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), a...
Learning according to the structural risk minimization principle can be naturally expressed as an Iv...
This article gives a short introduction to the main ideas of statistical learning theory, support ve...
During the past decade there has been an explosion in computation and information tech-nology. With ...
The main goal of this course is to study the generalization ability of a number of popular machine l...
Regularization Networks and Support Vector Machines are techniques for solv-ing certain problems of ...
The foundations of Support Vector Machines (SVM) have been developed by Vapnik and are gaining popul...
In this chapter we introduce basic concepts and ideas of the Support Vector Machines (SVM). In the f...
The paper introduces some generalizations of Vapnik's method of structural risk minimisation (S...
Many recently proposed learning algorithms are clearly inspired by Support Vector Machines. Some of ...
Statistical learning theory provides the theoretical basis for many of today's machine learning algo...
Regularization Networks and Support Vector Machines are techniques for solving certain problems of...
Statistical Learning refers to statistical aspects of automated extraction of regularities (structur...
We briefly describe the main ideas of statistical learning theory, support vector machines, and kern...
The Vicinal Risk Minimization principle establishes a bridge between generative models and methods d...
We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), a...
Learning according to the structural risk minimization principle can be naturally expressed as an Iv...
This article gives a short introduction to the main ideas of statistical learning theory, support ve...
During the past decade there has been an explosion in computation and information tech-nology. With ...
The main goal of this course is to study the generalization ability of a number of popular machine l...