عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
INTERNATIONAL STANDARD BOOK NUMBER
(Number (ISBN
0819478725
(Number (ISBN
9780819478726
Erroneous ISBN
0819459879
Erroneous ISBN
9780819459879
NATIONAL BIBLIOGRAPHY NUMBER
Number
dltt
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Artificial neural networks
General Material Designation
[Book]
Other Title Information
an introduction /
First Statement of Responsibility
Kevin L. Priddy and Paul E. Keller.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Bellingham, Wash. :
Name of Publisher, Distributor, etc.
SPIE,
Date of Publication, Distribution, etc.
c2005.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (ix, 165 p. :
Other Physical Details
ill.)
SERIES
Series Title
Tutorial texts in optical engineering ;
ISSN of Series
v. TT68
GENERAL NOTES
Text of Note
Title from PDF t.p. (viewed 8/23/09).
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references (p. [151]-162) and index.
CONTENTS NOTE
Text of Note
Chapter 1. Introduction. 1.1. The neuron -- 1.2. Modeling neurons -- 1.3. The feedforward neural network -- 1.4. Historical perspective on computing with artificial neurons.
Text of Note
Appendix A. The feedforward neural network. A.1. Mathematics of the feedforward process -- A.2. The backpropagation algorithm -- A.3. Alternatives to backpropagation.
Text of Note
Appendix B. Feature saliency.
Text of Note
Appendix C. Matlab code for various neural networks. C.1. Matlab code for principal components normalization -- C.2. Hopfield network -- C.3. Generalized neural network -- C.4. Generalized neural network example -- C.5. ART-like network -- C.6. Simple perceptron algorithm -- C.7. Kohonen self-organizing feature map.
Text of Note
Appendix D. Glossary of terms -- References -- Index.
Text of Note
Chapter 10. A plethora of applications. 10.1. Function approximation -- 10.2. Function approximation-Boston housing example -- 10.3. Function approximation-cardiopulmonary modeling -- 10.4. Pattern recognition-tree classifier example -- 10.5. Pattern recognition-handwritten number recognition example -- 10.6. Pattern recognition-electronic nose example -- 10.7. Pattern recognition-airport scanner texture recognition example -- 10.8. Self organization-serial killer data-mining example -- 10.9. Pulse-coupled neural networks-image segmentation example.
Text of Note
Chapter 11. Dealing with limited amounts of data. 11.1. K-fold cross-validation -- 11.2. Leave-one-out cross-validation -- 11.3. Jackknife resampling -- 11.4. Bootstrap resampling.
Text of Note
Chapter 2. Learning methods. 2.1. Supervised training methods -- 2.2. Unsupervised training methods.
Text of Note
Chapter 3. Data normalization. 3.1. Statistical or Z-score normalization -- 3.2. Min-max normalization -- 3.3. Sigmoidal or SoftMax normalization -- 3.4. Energy normalization -- 3.5. Principal components normalization.
Text of Note
Chapter 4. Data collection, preparation, labeling, and input coding. 4.1. Data collection -- 4.2. Feature selection and extraction.
Text of Note
Chapter 5. Output coding. 5.1. Classifier coding -- 5.2. Estimator coding.
Text of Note
Chapter 6. Post-processing.
Text of Note
Chapter 7. Supervised training methods. 7.1. The effects of training data on neural network performance -- 7.2. Rules of thumb for training neural networks -- 7.3. Training and testing.
Text of Note
Chapter 8. Unsupervised training methods. 8.1. Self-organizing maps (SOMs) -- 8.2. Adaptive resonance theory network.
Text of Note
Chapter 9. Recurrent neural networks. 9.1. Hopfield neural networks -- 9.2. The bidirectional associative memory (BAM) -- 9.3. The generalized linear neural network -- 9.4. Real-time recurrent network -- 9.5. Elman recurrent network.
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SUMMARY OR ABSTRACT
Text of Note
This tutorial text provides the reader with an understanding of artificial neural networks (ANNs) and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed and the data collection processes, to the many ways ANNs are being used today. The material is presented with a minimum of math (although the mathematical details are included in the appendices for interested readers), and with a maximum of hands-on experience. All specialized terms are included in a glossary. The result is a highly readable text that will teach the engineer the guiding principles necessary to use and apply artificial neural networks.
OTHER EDITION IN ANOTHER MEDIUM
International Standard Book Number
0819459879
TOPICAL NAME USED AS SUBJECT
Neural networks (Computer science)
PERSONAL NAME - PRIMARY RESPONSIBILITY
Priddy, Kevin L.
PERSONAL NAME - ALTERNATIVE RESPONSIBILITY
Keller, Paul E.
CORPORATE BODY NAME - ALTERNATIVE RESPONSIBILITY
Society of Photo-optical Instrumentation Engineers.
ORIGINATING SOURCE
Date of Transaction
20110427124820.0
ELECTRONIC LOCATION AND ACCESS
Electronic name
مطالعه متن کتاب [Book]
Y
