Feedforward Neural Network Methodology by Terrence L Fine
The decade prior to publication has seen an explosive growth in com- tational speed and memory and a rapid enrichment in our understa- ing of arti?cial neural networks. These two factors have cooperated to at last provide systems engineers and statisticians with a working, prac- cal, and successful ability to routinely make accurate complex, nonlinear models of such ill-understood phenomena as physical, economic, social, and information-based time series and signals and of the patterns h- den in high-dimensional data. The models are based closely on the data itself and require only little prior understanding of the stochastic mec- nisms underlying these phenomena. Among these models, the feedforward neural networks, also called multilayer perceptrons, have lent themselves to the design of the widest range of successful forecasters, pattern clas- ?ers, controllers, and sensors. In a number of problems in optical character recognition and medical diagnostics, such systems provide state-of-the-art performance and such performance is also expected in speech recognition applications. The successful application of feedforward neural networks to time series forecasting has been multiply demonstrated and quite visibly so in the formation of market funds in which investment decisions are based largely on neural network-based forecasts of performance. The purpose of this monograph, accomplished by exposing the meth- ology driving these developments, is to enable you to engage in these - plications and, by being brought to several research frontiers, to advance the methodology itself.-
Pattern Recognition and Machine Learning
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Bayesian Networks and Decision Graphs
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Novelty, Information and Surprise
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Computational Methods in Biometric Authentication
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Statistical Image Processing and Multidimensional Modeling
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The Practice of Time Series Analysis
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Computer Intrusion Detection and Network Monitoring
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Probabilistic Networks and Expert Systems
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Information Theoretic Learning
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Estimation of Dependences Based on Empirical Data
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Probabilistic Conditional Independence Structures
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Information and Complexity in Statistical Modeling
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Statistical and Inductive Inference by Minimum Message Length
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The Cross-Entropy Method
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Cumulative Sum Charts and Charting for Quality Improvement
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Nonlinear Dimensionality Reduction
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Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
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Support Vector Machines
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The Nature of Statistical Learning Theory
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Sequential Monte Carlo Methods in Practice
From the reviews:
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
"..Fine must be congratulated for a coherent presentation of carefully selected material. Given the diversity of the field, this represented a serious challenge. Again, Feeforward Neural Network Methodlogy is an excellent reference for whoever wants to be brought to the frontier of research. I enthusiastically recommend it."
| SKU | Unavailable |
| ISBN 13 | 9780387987453 |
| ISBN 10 | 0387987452 |
| Title | Feedforward Neural Network Methodology |
| Author | Terrence L Fine |
| Series | Information Science And Statistics |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Springer-Verlag New York Inc. |
| Year published | 1999-06-11 |
| Number of pages | 340 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |