Bayesian techniques have been developed over many years in a range of dierent elds, but have only recently been applied to the problem of learning in neural networks. As well as providing a consistent framework for statistical pattern recognition, the Bayesian approach oers a number of practical advantages including a potential solution to the problem of over- tting. This chapter aims to provide an introductory overview of the application of Bayesian methods to neural networks. It assumes the reader is familiar with standard feed-forward network models and how to train them using conventional techniques.
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
summary:Bayesian probability theory provides a framework for data modeling. In this framework it is ...
This is the second episode of the Bayesian saga started with the tutorial on the Bayesian probabilit...
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 different fields, but have onl...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
Summary The application of the Bayesian learning paradigm to neural networks results in a flexi-ble ...
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...
The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of the topi...
This paper reviews the Bayesian approach to learning in neural networks, then introduces a new adapt...
Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of challenging ...
AbstractWe describe two specific examples of neural-Bayesian approaches for complex modeling tasks: ...
this article may suggest that the research reported herein may be more fundamental than it really is...
A practical method for Bayesian training of feed-forward neural networks using sophisticated Monte C...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
summary:Bayesian probability theory provides a framework for data modeling. In this framework it is ...
This is the second episode of the Bayesian saga started with the tutorial on the Bayesian probabilit...
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 different fields, but have onl...
We give a short review on Bayesian techniques for neural networks and demonstrate the advantages of ...
Summary The application of the Bayesian learning paradigm to neural networks results in a flexi-ble ...
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...
The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of the topi...
This paper reviews the Bayesian approach to learning in neural networks, then introduces a new adapt...
Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of challenging ...
AbstractWe describe two specific examples of neural-Bayesian approaches for complex modeling tasks: ...
this article may suggest that the research reported herein may be more fundamental than it really is...
A practical method for Bayesian training of feed-forward neural networks using sophisticated Monte C...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
summary:Bayesian probability theory provides a framework for data modeling. In this framework it is ...
This is the second episode of the Bayesian saga started with the tutorial on the Bayesian probabilit...