The Resource Advances in pattern recognition systems using neural network technologies, edited by I. Guyon, P. S. P. Wang

Advances in pattern recognition systems using neural network technologies, edited by I. Guyon, P. S. P. Wang

Label
Advances in pattern recognition systems using neural network technologies
Title
Advances in pattern recognition systems using neural network technologies
Statement of responsibility
edited by I. Guyon, P. S. P. Wang
Contributor
Editor
Subject
Genre
Language
  • eng
  • eng
Summary
Contents: A Connectionist Approach to Speech Recognition (Y Bengio); Signature Verification Using a "Siamese" Time Delay Neural Network (J Bromley et al.); Boosting Performance in Neural Networks (H Drucker et al.); An Integrated Architecture for Recognition of Totally Unconstrained Handwritten Numerals (A Gupta et al.); Time-Warping Network: A Neural Approach to Hidden Markov Model Based Speech Recognition (E Levin et al.); Computing Optical Flow with a Recurrent Neural Network (H Li & J Wang); Integrated Segmentation and Recognition through Exhaustive Scans or Learned Saccadic Jumps (G L Mar
Member of
Cataloging source
MiAaPQ
Dewey number
668.385
Illustrations
illustrations
Index
no index present
Language note
English
LC call number
QA76.87
LC item number
.A383 1993
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Guyon, I.
  • Wang, P. S. P.
Series statement
Series in Machine Perception and Artificial Intelligence
Series volume
Volume 7
http://library.link/vocab/subjectName
  • Neural networks (Computer science)
  • Pattern recognition systems
  • Artificial intelligence
Label
Advances in pattern recognition systems using neural network technologies, edited by I. Guyon, P. S. P. Wang
Instantiates
Publication
Copyright
Note
Description based upon print version of record
Bibliography note
Includes bibliographical references at the end of each chapters
Carrier category
online resource
Carrier category code
cr
Content category
text
Content type code
txt
Contents
  • Contents; Preface; A Connectionist Approach to Speech Recognition; 1. Introduction; 2. Integrating Prior Knowledge and Learning; 2.1. Input representation and coding; 2.2. Output coding; 2.3. Architecture; 3. Connectionist Architectures for Speech Recognition; 3.1. Static isolated word recognizer; 3.2. Static fixed window; 3.3. Time-delay neural network (TDNN); 3.4. Segment-based classification; 3.5. Recurrent nets; 3.5.1. Problems with training of recurrent networks; 4. Hybrids of Connectionist Models and Stastical Models; 4.1. A New approach to density estimation with neural networks
  • 4.2. A Hybrid of neural network and discriminant Hidden Markov model5. Conclusion; Acknowledgements; References; Signature Verification using a ""Siamese"" Time Delay Neural Network; 1. Introduction; 1.1. NCR requirements; 2. Data; 2.1. Data sets; 3. Neural Networks Architectures; 4. Signature Preprocessing; 5. Training the Neural Networks; 5.1. First round of training; 5.2. Second round of training; 6. Testing; 6.1. First round; 6.2. Second round; 6.3. Zero-Effort forgeries; 6.4. The 80 byte constraint; 6.5. Comments on the testing; 7. Conclusions; Acknowledgements; References
  • Off Line Recognition of Handwritten Postal Words using Neural Networks1. Introduction; 2. Recognition of Handwritten Zip Codes; 2.1. Shortest path segmentation; 2.2. Image preprocessing; 2.3. Cut generation; 2.4. Graph representation of the segmentation process; 2.5. Neural network; 2.6. Neural network training; 2.7. Use of a ZIP code lexico; 2.8. Results; 3. Recognition of Handwritten Words; 3.1. Image preprocessing; 3.2. The Neural network; 3.3. Segmentation graph; 3.4. Results; 4. Conclusions; Acknowledgements; References; Boosting Performance in Neural Networks; 1. Introduction
  • 2. Theoritical Background3. A Deformation Model; 4. Network Architectures; 5. Training Algorithm; 6. Results on UPS Database; 7. Results on NIST Databases; 8. Using Sieving to Reduce Evaluation Time; 9. Discussion and Conclusion; Acknowledgements; References; Multi-Modular Neural Network Architectures: Applications in Optical Character and Human Face Recognition; 1. Introduction; 2. Classifiers; 2.1. Neural network models; 2.1.1. TDNN; 2.1.2. LVQ; 2.1.3. RBF; 2.2. k-Nearest neighbor; 3. Multi-Modular Architectures; 3.1. Modularity
  • 3.2. Multi-Modular feature extraction-classification architectures3.3. Segmentation; 4. Decision and Rejection Criteria; 4.1. Introduction; 4.2. MLP; 4.3. LVQ; 4.4. RBF; 5. Optical Character Recognition; 5.1. The Databases; 5.2. The Architectures; 5.2.1. Introduction; 5.2.2. MLP-encoder; 5.2.3. TDNN-LeNotre; 5.2.4. TDNN-LeNet; 5.2.5 k-nn; 5.2.6. LVQ; 5.2.7. RBF; 5.2.8. RBF-coop; 5.3. Classification Performances; 5.3.1. Effect of modularity; 5.3.2. Effect of cooperation; 5.3.3. Conclusion; 5.4. Rejection; 5.4.1. Method; 5.4.2. Comparison of criteria; 5.4.3. Comparison of architectures
  • 5.4.4. Evaluation of global performances
Dimensions
unknown
Extent
1 online resource (329 p.)
Form of item
online
Isbn
9789812797926
Media category
computer
Media type code
c
Specific material designation
remote
System control number
  • (CKB)2550000001254580
  • (EBL)1664100
  • (SSID)ssj0001209628
  • (PQKBManifestationID)11668949
  • (PQKBTitleCode)TC0001209628
  • (PQKBWorkID)11174686
  • (PQKB)11513777
  • (MiAaPQ)EBC1664100
  • (EXLCZ)992550000001254580
Label
Advances in pattern recognition systems using neural network technologies, edited by I. Guyon, P. S. P. Wang
Publication
Copyright
Note
Description based upon print version of record
Bibliography note
Includes bibliographical references at the end of each chapters
Carrier category
online resource
Carrier category code
cr
Content category
text
Content type code
txt
Contents
  • Contents; Preface; A Connectionist Approach to Speech Recognition; 1. Introduction; 2. Integrating Prior Knowledge and Learning; 2.1. Input representation and coding; 2.2. Output coding; 2.3. Architecture; 3. Connectionist Architectures for Speech Recognition; 3.1. Static isolated word recognizer; 3.2. Static fixed window; 3.3. Time-delay neural network (TDNN); 3.4. Segment-based classification; 3.5. Recurrent nets; 3.5.1. Problems with training of recurrent networks; 4. Hybrids of Connectionist Models and Stastical Models; 4.1. A New approach to density estimation with neural networks
  • 4.2. A Hybrid of neural network and discriminant Hidden Markov model5. Conclusion; Acknowledgements; References; Signature Verification using a ""Siamese"" Time Delay Neural Network; 1. Introduction; 1.1. NCR requirements; 2. Data; 2.1. Data sets; 3. Neural Networks Architectures; 4. Signature Preprocessing; 5. Training the Neural Networks; 5.1. First round of training; 5.2. Second round of training; 6. Testing; 6.1. First round; 6.2. Second round; 6.3. Zero-Effort forgeries; 6.4. The 80 byte constraint; 6.5. Comments on the testing; 7. Conclusions; Acknowledgements; References
  • Off Line Recognition of Handwritten Postal Words using Neural Networks1. Introduction; 2. Recognition of Handwritten Zip Codes; 2.1. Shortest path segmentation; 2.2. Image preprocessing; 2.3. Cut generation; 2.4. Graph representation of the segmentation process; 2.5. Neural network; 2.6. Neural network training; 2.7. Use of a ZIP code lexico; 2.8. Results; 3. Recognition of Handwritten Words; 3.1. Image preprocessing; 3.2. The Neural network; 3.3. Segmentation graph; 3.4. Results; 4. Conclusions; Acknowledgements; References; Boosting Performance in Neural Networks; 1. Introduction
  • 2. Theoritical Background3. A Deformation Model; 4. Network Architectures; 5. Training Algorithm; 6. Results on UPS Database; 7. Results on NIST Databases; 8. Using Sieving to Reduce Evaluation Time; 9. Discussion and Conclusion; Acknowledgements; References; Multi-Modular Neural Network Architectures: Applications in Optical Character and Human Face Recognition; 1. Introduction; 2. Classifiers; 2.1. Neural network models; 2.1.1. TDNN; 2.1.2. LVQ; 2.1.3. RBF; 2.2. k-Nearest neighbor; 3. Multi-Modular Architectures; 3.1. Modularity
  • 3.2. Multi-Modular feature extraction-classification architectures3.3. Segmentation; 4. Decision and Rejection Criteria; 4.1. Introduction; 4.2. MLP; 4.3. LVQ; 4.4. RBF; 5. Optical Character Recognition; 5.1. The Databases; 5.2. The Architectures; 5.2.1. Introduction; 5.2.2. MLP-encoder; 5.2.3. TDNN-LeNotre; 5.2.4. TDNN-LeNet; 5.2.5 k-nn; 5.2.6. LVQ; 5.2.7. RBF; 5.2.8. RBF-coop; 5.3. Classification Performances; 5.3.1. Effect of modularity; 5.3.2. Effect of cooperation; 5.3.3. Conclusion; 5.4. Rejection; 5.4.1. Method; 5.4.2. Comparison of criteria; 5.4.3. Comparison of architectures
  • 5.4.4. Evaluation of global performances
Dimensions
unknown
Extent
1 online resource (329 p.)
Form of item
online
Isbn
9789812797926
Media category
computer
Media type code
c
Specific material designation
remote
System control number
  • (CKB)2550000001254580
  • (EBL)1664100
  • (SSID)ssj0001209628
  • (PQKBManifestationID)11668949
  • (PQKBTitleCode)TC0001209628
  • (PQKBWorkID)11174686
  • (PQKB)11513777
  • (MiAaPQ)EBC1664100
  • (EXLCZ)992550000001254580

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