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Artificial neural networks = an introduction /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Artificial neural networks/ Kevin L. Priddy and Paul E. Keller.
其他題名:	an introduction /
作者:	Priddy, Kevin L.
其他作者:	Keller, Paul E.
出版者:	Bellingham, Wash. (1000 20th St. Bellingham WA 98225-6705 USA) :SPIE, : c2005.,
面頁冊數:	1 online resource (ix, 165 p. : ill.) :digital file. :
附註:	"SPIE digital library."
標題:	Neural networks (Computer science) -
電子資源:	http://dx.doi.org/10.1117/3.633187
電子資源:	http://www.loc.gov/catdir/toc/ecip0516/2005021833.html
ISBN:	9780819478726 (electronic)

Artificial neural networks = an introduction /
Priddy, Kevin L.

Artificial neural networksan introduction /[electronic resource] :Kevin L. Priddy and Paul E. Keller. - Bellingham, Wash. (1000 20th St. Bellingham WA 98225-6705 USA) :SPIE,c2005. - 1 online resource (ix, 165 p. : ill.) :digital file. - Tutorial texts in optical engineering ;v. TT68. - SPIE tutorial texts ;TT06..

"SPIE digital library."

Includes bibliographical references (p. [151]-162) and index.

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.

Restricted to subscribers or individual electronic text purchasers.

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.

Mode of access: World Wide Web.

ISBN: 9780819478726 (electronic)

Standard No.: 10.1117/3.633187doiSubjects--Topical Terms:

528588
Neural networks (Computer science)

LC Class. No.: QA76.87 / .P736 2005e

Dewey Class. No.: 006.3/2

Artificial neural networks = an introduction /
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245 1 0 $a Artificial neural networks $h [electronic resource] : $b an introduction / $c Kevin L. Priddy and Paul E. Keller.
260 $a Bellingham, Wash. (1000 20th St. Bellingham WA 98225-6705 USA) : $b SPIE, $c c2005.
300 $a 1 online resource (ix, 165 p. : ill.) : $b digital file.
490 1 $a Tutorial texts in optical engineering ; $v v. TT68
500 $a "SPIE digital library."
504 $a Includes bibliographical references (p. [151]-162) and index.
505 0 $a 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.
505 0 $a Chapter 2. Learning methods. 2.1. Supervised training methods -- 2.2. Unsupervised training methods.
505 0 $a 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.
505 0 $a Chapter 4. Data collection, preparation, labeling, and input coding. 4.1. Data collection -- 4.2. Feature selection and extraction.
505 0 $a Chapter 5. Output coding. 5.1. Classifier coding -- 5.2. Estimator coding.
505 0 $a Chapter 6. Post-processing.
505 0 $a 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.
505 0 $a Chapter 8. Unsupervised training methods. 8.1. Self-organizing maps (SOMs) -- 8.2. Adaptive resonance theory network.
505 0 $a 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.
505 0 $a 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.
505 0 $a 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.
505 0 $a Appendix A. The feedforward neural network. A.1. Mathematics of the feedforward process -- A.2. The backpropagation algorithm -- A.3. Alternatives to backpropagation.
505 0 $a Appendix B. Feature saliency.
505 0 $a 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.
505 0 $a Appendix D. Glossary of terms -- References -- Index.
506 $a Restricted to subscribers or individual electronic text purchasers.
520 3 $a 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.
530 $a Also available in print.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Title from PDF t.p. (viewed on 8/23/09).
650 0 $a Neural networks (Computer science) $3 528588
700 1 $a Keller, Paul E. $3 877288
710 2 $a Society of Photo-optical Instrumentation Engineers. $3 700253
830 0 $a SPIE tutorial texts ; $v TT06. $3 877241
856 4 0 $u http://dx.doi.org/10.1117/3.633187
856 4 1 $3 Table of contents $u http://www.loc.gov/catdir/toc/ecip0516/2005021833.html