Self-Organizing Neural Networks: Recent Advances and Applications
The Self-Organizing Map (SOM) is one of the most frequently used architectures for unsupervised artificial neural networks. Introduced by Teuvo Kohonen in the 1980s, SOMs have been developed as a very powerful method for visualization and unsupervised classification tasks by an active and innovative...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Heidelberg
Physica-Verlag HD
2002
|
| Schriftenreihe: | Studies in Fuzziness and Soft Computing
78 |
| Schlagworte: | |
| Online-Zugang: | FHI01 BTU01 Volltext |
| Zusammenfassung: | The Self-Organizing Map (SOM) is one of the most frequently used architectures for unsupervised artificial neural networks. Introduced by Teuvo Kohonen in the 1980s, SOMs have been developed as a very powerful method for visualization and unsupervised classification tasks by an active and innovative community of interna tional researchers. A number of extensions and modifications have been developed during the last two decades. The reason is surely not that the original algorithm was imperfect or inad equate. It is rather the universal applicability and easy handling of the SOM. Com pared to many other network paradigms, only a few parameters need to be arranged and thus also for a beginner the network leads to useful and reliable results. Never theless there is scope for improvements and sophisticated new developments as this book impressively demonstrates. The number of published applications utilizing the SOM appears to be unending. As the title of this book indicates, the reader will benefit from some of the latest the oretical developments and will become acquainted with a number of challenging real-world applications. Our aim in producing this book has been to provide an up to-date treatment of the field of self-organizing neural networks, which will be ac cessible to researchers, practitioners and graduated students from diverse disciplines in academics and industry. We are very grateful to the father of the SOMs, Professor Teuvo Kohonen for sup porting this book and contributing the first chapter |
| Beschreibung: | 1 Online-Ressource (XIV, 278 p. 465 illus., 5 illus. in color) |
| ISBN: | 9783790818109 |
| DOI: | 10.1007/978-3-7908-1810-9 |
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| 520 | |a The Self-Organizing Map (SOM) is one of the most frequently used architectures for unsupervised artificial neural networks. Introduced by Teuvo Kohonen in the 1980s, SOMs have been developed as a very powerful method for visualization and unsupervised classification tasks by an active and innovative community of interna tional researchers. A number of extensions and modifications have been developed during the last two decades. The reason is surely not that the original algorithm was imperfect or inad equate. It is rather the universal applicability and easy handling of the SOM. Com pared to many other network paradigms, only a few parameters need to be arranged and thus also for a beginner the network leads to useful and reliable results. Never theless there is scope for improvements and sophisticated new developments as this book impressively demonstrates. The number of published applications utilizing the SOM appears to be unending. As the title of this book indicates, the reader will benefit from some of the latest the oretical developments and will become acquainted with a number of challenging real-world applications. Our aim in producing this book has been to provide an up to-date treatment of the field of self-organizing neural networks, which will be ac cessible to researchers, practitioners and graduated students from diverse disciplines in academics and industry. We are very grateful to the father of the SOMs, Professor Teuvo Kohonen for sup porting this book and contributing the first chapter | ||
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| spelling | Self-Organizing Neural Networks Recent Advances and Applications edited by Udo Seiffert, Lakhmi C. Jain Heidelberg Physica-Verlag HD 2002 1 Online-Ressource (XIV, 278 p. 465 illus., 5 illus. in color) txt rdacontent c rdamedia cr rdacarrier Studies in Fuzziness and Soft Computing 78 The Self-Organizing Map (SOM) is one of the most frequently used architectures for unsupervised artificial neural networks. Introduced by Teuvo Kohonen in the 1980s, SOMs have been developed as a very powerful method for visualization and unsupervised classification tasks by an active and innovative community of interna tional researchers. A number of extensions and modifications have been developed during the last two decades. The reason is surely not that the original algorithm was imperfect or inad equate. It is rather the universal applicability and easy handling of the SOM. Com pared to many other network paradigms, only a few parameters need to be arranged and thus also for a beginner the network leads to useful and reliable results. Never theless there is scope for improvements and sophisticated new developments as this book impressively demonstrates. The number of published applications utilizing the SOM appears to be unending. As the title of this book indicates, the reader will benefit from some of the latest the oretical developments and will become acquainted with a number of challenging real-world applications. Our aim in producing this book has been to provide an up to-date treatment of the field of self-organizing neural networks, which will be ac cessible to researchers, practitioners and graduated students from diverse disciplines in academics and industry. We are very grateful to the father of the SOMs, Professor Teuvo Kohonen for sup porting this book and contributing the first chapter Computer Science Artificial Intelligence (incl. Robotics) Computation by Abstract Devices Statistical Physics, Dynamical Systems and Complexity Computer science Computers Artificial intelligence Statistical physics Dynamical systems Selbstorganisierende Karte (DE-588)4305302-6 gnd rswk-swf 1\p (DE-588)4143413-4 Aufsatzsammlung gnd-content Selbstorganisierende Karte (DE-588)4305302-6 s 2\p DE-604 Seiffert, Udo edt Jain, Lakhmi C. edt Erscheint auch als Druck-Ausgabe 9783662003435 https://doi.org/10.1007/978-3-7908-1810-9 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Self-Organizing Neural Networks Recent Advances and Applications Computer Science Artificial Intelligence (incl. Robotics) Computation by Abstract Devices Statistical Physics, Dynamical Systems and Complexity Computer science Computers Artificial intelligence Statistical physics Dynamical systems Selbstorganisierende Karte (DE-588)4305302-6 gnd |
| subject_GND | (DE-588)4305302-6 (DE-588)4143413-4 |
| title | Self-Organizing Neural Networks Recent Advances and Applications |
| title_auth | Self-Organizing Neural Networks Recent Advances and Applications |
| title_exact_search | Self-Organizing Neural Networks Recent Advances and Applications |
| title_full | Self-Organizing Neural Networks Recent Advances and Applications edited by Udo Seiffert, Lakhmi C. Jain |
| title_fullStr | Self-Organizing Neural Networks Recent Advances and Applications edited by Udo Seiffert, Lakhmi C. Jain |
| title_full_unstemmed | Self-Organizing Neural Networks Recent Advances and Applications edited by Udo Seiffert, Lakhmi C. Jain |
| title_short | Self-Organizing Neural Networks |
| title_sort | self organizing neural networks recent advances and applications |
| title_sub | Recent Advances and Applications |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Computation by Abstract Devices Statistical Physics, Dynamical Systems and Complexity Computer science Computers Artificial intelligence Statistical physics Dynamical systems Selbstorganisierende Karte (DE-588)4305302-6 gnd |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Computation by Abstract Devices Statistical Physics, Dynamical Systems and Complexity Computer science Computers Artificial intelligence Statistical physics Dynamical systems Selbstorganisierende Karte Aufsatzsammlung |
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