This is the second episode of the Bayesian saga started with the tutorial on the Bayesian probability. Its aim is showing in very informal terms how supervised learning can be interpreted from the Bayesian viewpoint. The focus is put on supervised learning of neural networks. The traditional approach to supervised neural network training is compared with the Bayesian perspective on supervised learning. A probabilistic interpretation is given to the traditional error function and to its minimization, to the phenomenon of overfitting and to the traditional countermeasures to prevent it. Finally, it is shown how the Bayesian approach solves the problem of assessing the performance of different network structures
Bayesian network models are widely used for supervised prediction tasks such as classi cation. Usua...
We propose a modular neural-network structure for imple-menting the Bayesian framework for learning ...
RÉSUMÉ: Les réseaux de neurones profonds sont capables de résoudre de nombreux problèmes d'apprentis...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
Many facets of Bayesian Modelling are firmly established in Machine Learning and give rise to state-...
Bayesian techniques have been developed over many years in a range of dierent elds, but have only re...
The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of the topi...
Three Bayesian ideas are presented for supervised adaptive classifiers. First, it is argued that the...
Conventional training methods for neural networks involve starting al a random location in the solut...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
Bayesian machine learning has gained tremendous attention in the machine learning community over the...
Since Bayesian learning for neural networks was introduced by MacKay it was applied to real world pr...
We show that many machine-learning algorithms are specific instances of a single algorithm called th...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of challenging ...
Bayesian network models are widely used for supervised prediction tasks such as classi cation. Usua...
We propose a modular neural-network structure for imple-menting the Bayesian framework for learning ...
RÉSUMÉ: Les réseaux de neurones profonds sont capables de résoudre de nombreux problèmes d'apprentis...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
Many facets of Bayesian Modelling are firmly established in Machine Learning and give rise to state-...
Bayesian techniques have been developed over many years in a range of dierent elds, but have only re...
The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of the topi...
Three Bayesian ideas are presented for supervised adaptive classifiers. First, it is argued that the...
Conventional training methods for neural networks involve starting al a random location in the solut...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
Bayesian machine learning has gained tremendous attention in the machine learning community over the...
Since Bayesian learning for neural networks was introduced by MacKay it was applied to real world pr...
We show that many machine-learning algorithms are specific instances of a single algorithm called th...
Bayesian techniques have been developed over many years in a range of different fields, but have onl...
Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of challenging ...
Bayesian network models are widely used for supervised prediction tasks such as classi cation. Usua...
We propose a modular neural-network structure for imple-menting the Bayesian framework for learning ...
RÉSUMÉ: Les réseaux de neurones profonds sont capables de résoudre de nombreux problèmes d'apprentis...