Hybrid methods in pattern recognition /:
The field of pattern recognition has seen enormous progress since its beginnings almost 50 years ago. A large number of different approaches have been proposed. Hybrid methods aim at combining the advantages of different paradigms within a single system. Hybrid Methods in Pattern Recognition is a co...
Saved in:
| Other Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
River Edge, N.J. :
World Scientific,
©2002.
|
| Series: | Series in machine perception and artificial intelligence ;
v. 47. |
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | The field of pattern recognition has seen enormous progress since its beginnings almost 50 years ago. A large number of different approaches have been proposed. Hybrid methods aim at combining the advantages of different paradigms within a single system. Hybrid Methods in Pattern Recognition is a collection of articles describing recent progress in this emerging field. It covers topics such as the combination of neural nets with fuzzy systems or hidden Markov models, neural networks for the processing of symbolic data structures, hybrid methods in data mining, the combination of symbolic and s. |
| Physical Description: | 1 online resource (xii, 324 pages) : illustrations |
| Bibliography: | Includes bibliographical references. |
| ISBN: | 9812778144 9789812778147 |
Staff View
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| 505 | 0 | |a Preface; Contents; Neuro-Fuzzy Systems; Chapter 1 Fuzzification of Neural Networks for Classification Problems; Neural Networks for Structural Pattern Recognition; Chapter 2 Adaptive Graphic Pattern Recognition: Foundations and Perspectives; Chapter 3 Adaptive Self-Organizing Map in the Graph Domain; Clustering for Hybrid Systems; Chapter 4 From Numbers to Information Granules: A Study in Unsupervised Learning and Feature Analysis; Combining Neural Networks and Hidden Markov Models; Chapter 5 Combination of Hidden Markov Models and Neural Networks for Hybrid Statistical Pattern Recognition. | |
| 505 | 8 | |a Chapter 6 From Character to Sentences: A Hybrid Neuro-Markovian System for On-Line Handwriting RecognitionMultiple Classifier Systems; Chapter 7 Multiple Classifier Combination: Lessons and Next Steps; Chapter 8 Design of Multiple Classifier Systems; Chapter 9 Fusing Neural Networks Through Fuzzy Integration; Applications of Hybrid Systems; Chapter 10 Hybrid Data Mining Methods in Image Processing; Chapter 11 Robust Fingerprint Identification Based on Hybrid Pattern Recognition Methods; Chapter 12 Text Categorization Using Learned Document Features. | |
| 520 | |a The field of pattern recognition has seen enormous progress since its beginnings almost 50 years ago. A large number of different approaches have been proposed. Hybrid methods aim at combining the advantages of different paradigms within a single system. Hybrid Methods in Pattern Recognition is a collection of articles describing recent progress in this emerging field. It covers topics such as the combination of neural nets with fuzzy systems or hidden Markov models, neural networks for the processing of symbolic data structures, hybrid methods in data mining, the combination of symbolic and s. | ||
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| contents | Preface; Contents; Neuro-Fuzzy Systems; Chapter 1 Fuzzification of Neural Networks for Classification Problems; Neural Networks for Structural Pattern Recognition; Chapter 2 Adaptive Graphic Pattern Recognition: Foundations and Perspectives; Chapter 3 Adaptive Self-Organizing Map in the Graph Domain; Clustering for Hybrid Systems; Chapter 4 From Numbers to Information Granules: A Study in Unsupervised Learning and Feature Analysis; Combining Neural Networks and Hidden Markov Models; Chapter 5 Combination of Hidden Markov Models and Neural Networks for Hybrid Statistical Pattern Recognition. Chapter 6 From Character to Sentences: A Hybrid Neuro-Markovian System for On-Line Handwriting RecognitionMultiple Classifier Systems; Chapter 7 Multiple Classifier Combination: Lessons and Next Steps; Chapter 8 Design of Multiple Classifier Systems; Chapter 9 Fusing Neural Networks Through Fuzzy Integration; Applications of Hybrid Systems; Chapter 10 Hybrid Data Mining Methods in Image Processing; Chapter 11 Robust Fingerprint Identification Based on Hybrid Pattern Recognition Methods; Chapter 12 Text Categorization Using Learned Document Features. |
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| owner | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-862 DE-BY-FWS DE-863 DE-BY-FWS |
| physical | 1 online resource (xii, 324 pages) : illustrations |
| psigel | ZDB-4-EBA FWS_PDA_EBA ZDB-4-EBA |
| publishDate | 2002 |
| publishDateSearch | 2002 |
| publishDateSort | 2002 |
| publisher | World Scientific, |
| record_format | marc |
| series | Series in machine perception and artificial intelligence ; |
| series2 | Series in machine perception and artificial intelligence ; |
| spelling | Hybrid methods in pattern recognition / editors, H. Bunke, A. Kandel. River Edge, N.J. : World Scientific, ©2002. 1 online resource (xii, 324 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file rda Series in machine perception and artificial intelligence ; v. 47 Includes bibliographical references. Print version record. Preface; Contents; Neuro-Fuzzy Systems; Chapter 1 Fuzzification of Neural Networks for Classification Problems; Neural Networks for Structural Pattern Recognition; Chapter 2 Adaptive Graphic Pattern Recognition: Foundations and Perspectives; Chapter 3 Adaptive Self-Organizing Map in the Graph Domain; Clustering for Hybrid Systems; Chapter 4 From Numbers to Information Granules: A Study in Unsupervised Learning and Feature Analysis; Combining Neural Networks and Hidden Markov Models; Chapter 5 Combination of Hidden Markov Models and Neural Networks for Hybrid Statistical Pattern Recognition. Chapter 6 From Character to Sentences: A Hybrid Neuro-Markovian System for On-Line Handwriting RecognitionMultiple Classifier Systems; Chapter 7 Multiple Classifier Combination: Lessons and Next Steps; Chapter 8 Design of Multiple Classifier Systems; Chapter 9 Fusing Neural Networks Through Fuzzy Integration; Applications of Hybrid Systems; Chapter 10 Hybrid Data Mining Methods in Image Processing; Chapter 11 Robust Fingerprint Identification Based on Hybrid Pattern Recognition Methods; Chapter 12 Text Categorization Using Learned Document Features. The field of pattern recognition has seen enormous progress since its beginnings almost 50 years ago. A large number of different approaches have been proposed. Hybrid methods aim at combining the advantages of different paradigms within a single system. Hybrid Methods in Pattern Recognition is a collection of articles describing recent progress in this emerging field. It covers topics such as the combination of neural nets with fuzzy systems or hidden Markov models, neural networks for the processing of symbolic data structures, hybrid methods in data mining, the combination of symbolic and s. Pattern recognition systems. http://id.loc.gov/authorities/subjects/sh85098791 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Pattern Recognition, Automated https://id.nlm.nih.gov/mesh/D010363 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Reconnaissance des formes (Informatique) Réseaux neuronaux (Informatique) COMPUTERS Optical Data Processing. bisacsh Neural networks (Computer science) fast Pattern recognition systems fast Electronic books. Bunke, Horst, 1949- https://id.oclc.org/worldcat/entity/E39PCjtrWx4cfxRY4xpKqhfxwC Kandel, Abraham. https://id.oclc.org/worldcat/entity/E39PCjxCpFYvtQmyw9BT3fjBKb http://id.loc.gov/authorities/names/n79067239 Print version: Hybrid methods in pattern recognition. River Edge, N.J. : World Scientific, ©2002 9810248326 9789810248321 (DLC) 2005297883 (OCoLC)50196647 Series in machine perception and artificial intelligence ; v. 47. http://id.loc.gov/authorities/names/n91107585 |
| spellingShingle | Hybrid methods in pattern recognition / Series in machine perception and artificial intelligence ; Preface; Contents; Neuro-Fuzzy Systems; Chapter 1 Fuzzification of Neural Networks for Classification Problems; Neural Networks for Structural Pattern Recognition; Chapter 2 Adaptive Graphic Pattern Recognition: Foundations and Perspectives; Chapter 3 Adaptive Self-Organizing Map in the Graph Domain; Clustering for Hybrid Systems; Chapter 4 From Numbers to Information Granules: A Study in Unsupervised Learning and Feature Analysis; Combining Neural Networks and Hidden Markov Models; Chapter 5 Combination of Hidden Markov Models and Neural Networks for Hybrid Statistical Pattern Recognition. Chapter 6 From Character to Sentences: A Hybrid Neuro-Markovian System for On-Line Handwriting RecognitionMultiple Classifier Systems; Chapter 7 Multiple Classifier Combination: Lessons and Next Steps; Chapter 8 Design of Multiple Classifier Systems; Chapter 9 Fusing Neural Networks Through Fuzzy Integration; Applications of Hybrid Systems; Chapter 10 Hybrid Data Mining Methods in Image Processing; Chapter 11 Robust Fingerprint Identification Based on Hybrid Pattern Recognition Methods; Chapter 12 Text Categorization Using Learned Document Features. Pattern recognition systems. http://id.loc.gov/authorities/subjects/sh85098791 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Pattern Recognition, Automated https://id.nlm.nih.gov/mesh/D010363 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Reconnaissance des formes (Informatique) Réseaux neuronaux (Informatique) COMPUTERS Optical Data Processing. bisacsh Neural networks (Computer science) fast Pattern recognition systems fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85098791 http://id.loc.gov/authorities/subjects/sh90001937 https://id.nlm.nih.gov/mesh/D010363 https://id.nlm.nih.gov/mesh/D016571 |
| title | Hybrid methods in pattern recognition / |
| title_auth | Hybrid methods in pattern recognition / |
| title_exact_search | Hybrid methods in pattern recognition / |
| title_full | Hybrid methods in pattern recognition / editors, H. Bunke, A. Kandel. |
| title_fullStr | Hybrid methods in pattern recognition / editors, H. Bunke, A. Kandel. |
| title_full_unstemmed | Hybrid methods in pattern recognition / editors, H. Bunke, A. Kandel. |
| title_short | Hybrid methods in pattern recognition / |
| title_sort | hybrid methods in pattern recognition |
| topic | Pattern recognition systems. http://id.loc.gov/authorities/subjects/sh85098791 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Pattern Recognition, Automated https://id.nlm.nih.gov/mesh/D010363 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Reconnaissance des formes (Informatique) Réseaux neuronaux (Informatique) COMPUTERS Optical Data Processing. bisacsh Neural networks (Computer science) fast Pattern recognition systems fast |
| topic_facet | Pattern recognition systems. Neural networks (Computer science) Pattern Recognition, Automated Neural Networks, Computer Reconnaissance des formes (Informatique) Réseaux neuronaux (Informatique) COMPUTERS Optical Data Processing. Pattern recognition systems Electronic books. |
| work_keys_str_mv | AT bunkehorst hybridmethodsinpatternrecognition AT kandelabraham hybridmethodsinpatternrecognition |