Add to ORKGWe present new tools from probability theory that can be applied to the analysis of learning algorithms. These tools allow to derive new bounds on the generalization performance of learning algorithms and to propose alternative measures of the complexity of the learning task, which in turn can be used to derive new learning algorithms
A new scientific monograph developing significant new algorithmic foundations in machine learning th...
The exchange of ideas between computer science and statistical physics has advanced the understandin...
This article provides a critical assessment of the Gradual Learning Algorithm (GLA) for probabilisti...
We present new tools from probability theory that can be applied to the analysis of learning algorit...
The goal of statistical learning theory is to study, in a statistical framework, the properties of l...
We give an exposition of the ideas of statistical learning theory, followed by a discussion of how a...
Statistical learning theory provides the theoretical basis for many of today's machine learning algo...
Statistical Learning Theory now plays a more active role after the general analysis of learning pro...
AbstractSome basic issues in the statistical mechanics of learning from examples are reviewed. The a...
This paper discusses the applications of certain combinatorial and probabilistic techniques to the a...
Statistical learning theory was developed by Vapnik. It is a learning theory based on Vapnik-Chervon...
This paper argues that Bayesian probability theory is a general method for machine learning. From tw...
University of Technology Sydney. Faculty of Engineering and Information Technology.Statistical learn...
The main goal of this course is to study the generalization ability of a number of popular machine l...
A general mathematical framework is developed for learning algorithms. A learning task belongs to ei...
A new scientific monograph developing significant new algorithmic foundations in machine learning th...
The exchange of ideas between computer science and statistical physics has advanced the understandin...
This article provides a critical assessment of the Gradual Learning Algorithm (GLA) for probabilisti...
We present new tools from probability theory that can be applied to the analysis of learning algorit...
The goal of statistical learning theory is to study, in a statistical framework, the properties of l...
We give an exposition of the ideas of statistical learning theory, followed by a discussion of how a...
Statistical learning theory provides the theoretical basis for many of today's machine learning algo...
Statistical Learning Theory now plays a more active role after the general analysis of learning pro...
AbstractSome basic issues in the statistical mechanics of learning from examples are reviewed. The a...
This paper discusses the applications of certain combinatorial and probabilistic techniques to the a...
Statistical learning theory was developed by Vapnik. It is a learning theory based on Vapnik-Chervon...
This paper argues that Bayesian probability theory is a general method for machine learning. From tw...
University of Technology Sydney. Faculty of Engineering and Information Technology.Statistical learn...
The main goal of this course is to study the generalization ability of a number of popular machine l...
A general mathematical framework is developed for learning algorithms. A learning task belongs to ei...
A new scientific monograph developing significant new algorithmic foundations in machine learning th...
The exchange of ideas between computer science and statistical physics has advanced the understandin...
This article provides a critical assessment of the Gradual Learning Algorithm (GLA) for probabilisti...