Abstract
Bayesian techniques have been developed over many years in a range of different fields, 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 offers a number of practical advantages including a potential solution to the problem of over-fitting. 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.
| Original language | English |
|---|---|
| Title of host publication | Oxford Lectures on Neural Networks |
| Editors | L. Tarassenko, E. Rolls, D. Sherrington |
| Place of Publication | Oxford |
| Publisher | Oxford University Press |
| Publication status | Published - 1995 |
Keywords
- bayesian
- neural networks
- learning
- pattern recognition
Fingerprint
Dive into the research topics of 'Bayesian methods for neural networks'. Together they form a unique fingerprint.Research output
- 1 Technical report
-
Bayesian methods for neural networks
Bishop, C. M., 1995, Birmingham: Aston University, 18 p. (NCRG; no. 95/009).Research output: Preprint or Working paper › Technical report
File
Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver