Synopses & Reviews
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.
Review
"In recent years, there has been a growing awareness of the common ground between neural networks and statistics. This volume contains eight sophisticated papers that probe this interdisciplinary research. . .This collection of rigorous research papers should be a valuable resource for theoretical neural-network modelers."--Journal of Mathematical Psychology
Synopsis
No religion ever remains static: it affects and is in turn affected by material reality. In this book, Sharma examines the contours of this creative tension in contemporary Hinduism. Sharma attempts to raise self-awareness of this dimension of Hinduism to an unprecedented level. In this way,
he hopes, that in the context of modernization and globalization, Hindus will be able to make conscious choices that will keep their religion at the cutting edge of the contemporary world instead of the periphery.
Table of Contents
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. Artificial Neural Networks and Multivariate Statistics, E. B. Martin and A. J. Morris
Index