J. W. Kay / D. M. Titterington

Statistics and Neural Networks

Advances at the Interface
Herausgeber: Kay, J W; Titterington, D M

• Gebundenes Buch

Produktbeschreibung

Recent years have seen a growing awareness of the interface between statistical research and recent advances in neural computing and artifical neural networks. This book covers various aspects of current work in the area, drawing together contributions from authors who are leading researchers in the two fields. Their contributions show a strong awareness of the common ground and of the advantages to be gained by taking the wider perspective. Topics covered include: nonlinear approaches to discriminant analysis; information-theoretic neural networks for unsupervised learning; Radial Basis Function networks; techniques for optimizing predictions; approaches to the analysis of latent structure, including probabalistic 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 research interface is currently extremely active and this volume gives an authoritative overview of the area, its current status and directions for future research.

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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.

Produktdetails

• Verlag: Sydney University Press
• Seitenzahl: 280
• Erscheinungstermin: 6. April 2000
• Englisch
• Abmessung: 244mm x 164mm x 27mm
• Gewicht: 648g
• ISBN-13: 9780198524229
• ISBN-10: 0198524226
• Artikelnr.: 21819874

Inhaltsangabe

Flexible discriminant and mixture models
Neural networks for unsupervised learning based on information theory
Radial basis function networks and statistics
Robust prediction in many-parameter models
Density networks
Latent variable models and data visualisation
Analysis of latent structure models with multidimensional latent variables
Artificial neural networks and multivariate statistics