rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Artificial neural networks
    sub title: an introduction
  name:
    name part: Priddy, Kevin L.
    role:
      role term: creator
  name:
    name part: Keller, Paul E.
  name:
    name part: Society of Photo-optical Instrumentation Engineers
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: wau
    date issued: 2005
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (ix, 165 pages : illustrations) : digital file.
  abstract: 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.
  table of contents: Appendix A. The feedforward neural network. A.1. Mathematics of the feedforward process -- A.2. The backpropagation algorithm -- A.3. Alternatives to backpropagation.
  table of contents: Appendix B. Feature saliency.
  table of contents: 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.
  table of contents: Appendix D. Glossary of terms -- References -- Index.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: Chapter 2. Learning methods. 2.1. Supervised training methods -- 2.2. Unsupervised training methods.
  table of contents: 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.
  table of contents: Chapter 4. Data collection, preparation, labeling, and input coding. 4.1. Data collection -- 4.2. Feature selection and extraction.
  table of contents: Chapter 5. Output coding. 5.1. Classifier coding -- 5.2. Estimator coding.
  table of contents: Chapter 6. Post-processing.
  table of contents: 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.
  table of contents: Chapter 8. Unsupervised training methods. 8.1. Self-organizing maps (SOMs) -- 8.2. Adaptive resonance theory network.
  table of contents: 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.
  target audience: specialized
  note: Kevin L. Priddy and Paul E. Keller.
  note: "SPIE digital library."
  note: Electronic resource.
  note: Restricted to subscribers or individual electronic text purchasers.
  note: Also available in print.
  note: Mode of access: World Wide Web.
  note: System requirements: Adobe Acrobat Reader.
  note: Includes bibliographical references (pages [151]-162) and index.
  subject:
    topic: Neural networks (Computer science)
  classification: QA76.87 .P736 2005e
  classification: 006.3/2
  related item:
    identifier: (DLC) 2005021833
  related item:
    title info:
      title: SPIE tutorial texts ; TT68
  identifier: 9780819478726 (electronic)
  identifier: 0819459879 (print)
  identifier: 9780819459879 (print)
  identifier: https://dx.doi.org/10.1117/3.633187
  identifier: http://www.loc.gov/catdir/toc/ecip0516/2005021833.html
  related item:
    location:
      url: http://www.loc.gov/catdir/toc/ecip0516/2005021833.html
  access condition: Restricted to subscribers or individual electronic text purchasers.
  record info:
    record content source: CaBNVSL
    record creation date: 050729
    record change date: 20210728125154.0
    record identifier: in00003739557