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Statistics & Neural Networks: Advances at Interface by D.M. Titterington HC/DJ
US $22.00
ApproximatelyS$ 28.46
Condition:
Like New
A book in excellent condition. Cover is shiny and undamaged, and the dust jacket is included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear.
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Free local pickup from Woburn, Massachusetts, United States.
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Located in: Woburn, Massachusetts, United States
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eBay item number:396972592591
Item specifics
- Condition
- ISBN
- 9780198524229
About this product
Product Identifiers
Publisher
Oxford University Press, Incorporated
ISBN-10
0198524226
ISBN-13
9780198524229
eBay Product ID (ePID)
1688862
Product Key Features
Number of Pages
280 Pages
Publication Name
Statistics and Neural Networks : Advances at the Interface
Language
English
Subject
Probability & Statistics / General, Neural Networks
Publication Year
2000
Type
Textbook
Subject Area
Mathematics, Computers
Format
Hardcover
Dimensions
Item Height
0.8 in
Item Weight
21.2 Oz
Item Length
9.2 in
Item Width
6.1 in
Additional Product Features
Intended Audience
College Audience
LCCN
99-041248
Dewey Edition
21
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, " ... written by to experts in statistics and neural networks ... I acnsincerely recommend this book to every neural researcher - there is a lot tolearn here" IEEE Transactions and Neural Networks, "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, This nicely written book is recommended to all wishing to gain knowledge of the current status and trends in the area., "This impressive book ... a valuable source book for comprehensivve reviews of current developments in an exciting interface area between statistics and computer science" Statistician, "This impressive book ... a valuable source book for comprehensivve reviews of current developments in an exciting interface area between statistics and computer science" Statistician" ... 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, "This impressive book ... a valuable source book for comprehensivvereviews of current developments in an exciting interface area between statisticsand computer science" Statistician, "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
Illustrated
Yes
Dewey Decimal
519.5/0285/632
Table Of Content
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
Synopsis
There is currently much activity at the interface between statistics and artificial neural networks research. This involves both the transportation of modern but fairly conventional statistical ideas into the treatment of neural networks and the development, within the neural-computation community, of new approaches in a number of branches of statistics. The book will consist of a number of substantial pieces, by world leaders in this research, which will combine to give a broad overview of important current developments and likely future trends., 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., 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.
LC Classification Number
QA276.S78343 1999
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