Statistics and Neural Networks: Advances at the Interface

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.