Statistics and Neural Networks: Advances at the Interface

Hardcover | February 1, 2000

byJ. W. Kay, D. M. Titterington

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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 researchersin 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 BasisFunction 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 inindustrial 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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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 researchersin the two fields. Their contributions show ...

J. W. Kay is at University of Glasgow. D. M. Titterington is at University of Glasgow.

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Format:HardcoverPublished:February 1, 2000Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0198524226

ISBN - 13:9780198524229

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Table of Contents

Flexible discriminant and mixture modelsNeural networks for unsupervised learning based on information theoryRadial basis function networks and statisticsRobust prediction in many-parameter modelsDensity networksLatent variable models and data visualisationAnalysis of latent structure models with multidimensional latent variablesArtificial neural networks and multivariate statistics

Editorial Reviews

" ... written by to experts in statistics and neural networks ... I acn sincerely recommend this book to every neural researcher - there is a lot to learn here" IEEE Transactions and Neural Networks