Several recent contributions have suggested to consider neural networks, obtained through supervized learning, as Bayesian estimators. This is like placing oneself in the context of the decision theory. This helps propose a definition of a PAB algorithm, thus generalizing a definition proposed by Valiant in his learning theory. / Plusieurs contributions récentes ont proposé de considérer les réseaux connexionnistes, obtenus par apprentissage supervisé, comme des estimateurs bayésiens. Cela revient à se placer dans le cadre de la théorie de la décision. Cela permet aussi de proposer une définition d'un algorithme PAB, généralisant une définition proposée par Valiant dans sa théorie de l'apprentissage
AbstractWe describe two specific examples of neural-Bayesian approaches for complex modeling tasks: ...
summary:For general Bayes decision rules there are considered perceptron approximations based on suf...
This paper introduces Bayesian neural network based on Occams razor. Basic knowledge about neural ne...
We propose a modular neural-network structure for imple-menting the Bayesian framework for learning ...
Summary The application of the Bayesian learning paradigm to neural networks results in a flexi-ble ...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
Bayesian techniques have been developed over many years in a range of dierent elds, but have only re...
The problem of evaluating dierent learning rules and other statistical estimators is analysed. A new...
RÉSUMÉ: Les réseaux de neurones profonds sont capables de résoudre de nombreux problèmes d'apprentis...
The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of the topi...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
this article may suggest that the research reported herein may be more fundamental than it really is...
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning t...
textabstractRecently, Bayesian methods have been proposed for neural networks to solve regression an...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
AbstractWe describe two specific examples of neural-Bayesian approaches for complex modeling tasks: ...
summary:For general Bayes decision rules there are considered perceptron approximations based on suf...
This paper introduces Bayesian neural network based on Occams razor. Basic knowledge about neural ne...
We propose a modular neural-network structure for imple-menting the Bayesian framework for learning ...
Summary The application of the Bayesian learning paradigm to neural networks results in a flexi-ble ...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
Bayesian techniques have been developed over many years in a range of dierent elds, but have only re...
The problem of evaluating dierent learning rules and other statistical estimators is analysed. A new...
RÉSUMÉ: Les réseaux de neurones profonds sont capables de résoudre de nombreux problèmes d'apprentis...
The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of the topi...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
this article may suggest that the research reported herein may be more fundamental than it really is...
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning t...
textabstractRecently, Bayesian methods have been proposed for neural networks to solve regression an...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
AbstractWe describe two specific examples of neural-Bayesian approaches for complex modeling tasks: ...
summary:For general Bayes decision rules there are considered perceptron approximations based on suf...
This paper introduces Bayesian neural network based on Occams razor. Basic knowledge about neural ne...