Statistics and Neural Netorks Advances at the Interface [Hardcover]

In the area where statistics and neural networks meet there has been rapid growth in active research and the number of applications in which the resulting techniques can be used. Interest is growing as companies discover important and lucrative applications of the research to complex problems in areas of engineering, computer science, finance, and other subjects. This book gives up-to-the-minute coverage on the research developing at this interface, drawing together contributions by leading workers in the two fields. Their contributions show a strong awareness of the common ground of these two subjects and of the advantages to be gained by taking this wider perspective. Topics that are covered include: non-linear approaches to discriminant analysis, techniques for optimizing predictions, approaches to the analysis of latent structure, including probabilistic principal component analysis, density networks and the use of multiple latent variables, and a substantial chapter outlining techniques and their application in industrial case-studies. This volume is an authoritative voice on the current status, importance of applications, and directions for future research in this area of synergistic science and will be an invaluable resource for those presently working in statistics and neural computing.

Contributors
1. Flexible Discriminant and Mixture Models,Trevor Hastie, Robert Tibshirani, and Andreas Buja
2. Neural Networks for Unsupervised Learning Based on Information Theory,Jim Kay
3. Radial Basis Function Networks and Statistics,David Lowe
4. Robust Prediction in Many-parameter Models,Nathan Intrator
5. Density Networks,David J. C. MacKay and Mark N. Gibbs
6. Latent Variable Models and Data Visualisation,Christopher M. Bishop and Michael E. Tipping
7. Analysis of Latent Structure Models with Multidimensional Latent Variables,A. P. Dunmur and D. M. Titterington
8. Artificial³7