Pattern Classification: Neuro-fuzzy Methods and Their Comparison
Neural networks have a learning capability but analysis of a trained network is difficult. On the other hand, extraction of fuzzy rules is difficult but once they have been extracted, it is relatively easy to analyze the fuzzy system. This book solves the above problems by developing new learning pa...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
London
Springer London
2001
|
| Ausgabe: | 1st ed. 2001 |
| Schlagworte: | |
| Online-Zugang: | UBY01 Volltext |
| Zusammenfassung: | Neural networks have a learning capability but analysis of a trained network is difficult. On the other hand, extraction of fuzzy rules is difficult but once they have been extracted, it is relatively easy to analyze the fuzzy system. This book solves the above problems by developing new learning paradigms and architectures for neural networks and fuzzy systems. The book consists of two parts: Pattern Classification and Function Approximation. In the first part, based on the synthesis principle of the neural-network classifier: A new learning paradigm is discussed and classification performance and training time of the new paradigm for several real-world data sets are compared with those of the widely-used back-propagation algorithm; Fuzzy classifiers of different architectures based on fuzzy rules can be defined with hyperbox, polyhedral, or ellipsoidal regions. The book discusses the unified approach for training these fuzzy classifiers; The performance of the newly-developed fuzzy classifiers and the conventional classifiers such as nearest-neighbor classifiers and support vector machines are evaluated using several real-world data sets and their advantages and disadvantages are clarified. In the second part: Function approximation is discussed extending the discussions in the first part; Performance of the function approximators is compared. This book is aimed primarily at researchers and practitioners in the field of artificial intelligence and neural networks |
| Beschreibung: | 1 Online-Ressource (XIX, 327 p) |
| ISBN: | 9781447102854 |
| DOI: | 10.1007/978-1-4471-0285-4 |
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| 520 | |a Neural networks have a learning capability but analysis of a trained network is difficult. On the other hand, extraction of fuzzy rules is difficult but once they have been extracted, it is relatively easy to analyze the fuzzy system. This book solves the above problems by developing new learning paradigms and architectures for neural networks and fuzzy systems. The book consists of two parts: Pattern Classification and Function Approximation. In the first part, based on the synthesis principle of the neural-network classifier: A new learning paradigm is discussed and classification performance and training time of the new paradigm for several real-world data sets are compared with those of the widely-used back-propagation algorithm; Fuzzy classifiers of different architectures based on fuzzy rules can be defined with hyperbox, polyhedral, or ellipsoidal regions. The book discusses the unified approach for training these fuzzy classifiers; The performance of the newly-developed fuzzy classifiers and the conventional classifiers such as nearest-neighbor classifiers and support vector machines are evaluated using several real-world data sets and their advantages and disadvantages are clarified. In the second part: Function approximation is discussed extending the discussions in the first part; Performance of the function approximators is compared. This book is aimed primarily at researchers and practitioners in the field of artificial intelligence and neural networks | ||
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| discipline | Informatik |
| discipline_str_mv | Informatik |
| doi_str_mv | 10.1007/978-1-4471-0285-4 |
| edition | 1st ed. 2001 |
| format | Electronic eBook |
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| isbn | 9781447102854 |
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| spelling | Abe, Shigeo Verfasser aut Pattern Classification Neuro-fuzzy Methods and Their Comparison by Shigeo Abe 1st ed. 2001 London Springer London 2001 1 Online-Ressource (XIX, 327 p) txt rdacontent c rdamedia cr rdacarrier Neural networks have a learning capability but analysis of a trained network is difficult. On the other hand, extraction of fuzzy rules is difficult but once they have been extracted, it is relatively easy to analyze the fuzzy system. This book solves the above problems by developing new learning paradigms and architectures for neural networks and fuzzy systems. The book consists of two parts: Pattern Classification and Function Approximation. In the first part, based on the synthesis principle of the neural-network classifier: A new learning paradigm is discussed and classification performance and training time of the new paradigm for several real-world data sets are compared with those of the widely-used back-propagation algorithm; Fuzzy classifiers of different architectures based on fuzzy rules can be defined with hyperbox, polyhedral, or ellipsoidal regions. The book discusses the unified approach for training these fuzzy classifiers; The performance of the newly-developed fuzzy classifiers and the conventional classifiers such as nearest-neighbor classifiers and support vector machines are evaluated using several real-world data sets and their advantages and disadvantages are clarified. In the second part: Function approximation is discussed extending the discussions in the first part; Performance of the function approximators is compared. This book is aimed primarily at researchers and practitioners in the field of artificial intelligence and neural networks Artificial Intelligence Complexity Pattern Recognition Artificial intelligence Computational complexity Pattern recognition Mustererkennung (DE-588)4040936-3 gnd rswk-swf Neuro-Fuzzy-System (DE-588)4560677-8 gnd rswk-swf Mustererkennung (DE-588)4040936-3 s Neuro-Fuzzy-System (DE-588)4560677-8 s DE-604 Erscheint auch als Druck-Ausgabe 9781447110774 Erscheint auch als Druck-Ausgabe 9781852333522 Erscheint auch als Druck-Ausgabe 9781447102861 https://doi.org/10.1007/978-1-4471-0285-4 Verlag URL des Eerstveröffentlichers Volltext |
| spellingShingle | Abe, Shigeo Pattern Classification Neuro-fuzzy Methods and Their Comparison Artificial Intelligence Complexity Pattern Recognition Artificial intelligence Computational complexity Pattern recognition Mustererkennung (DE-588)4040936-3 gnd Neuro-Fuzzy-System (DE-588)4560677-8 gnd |
| subject_GND | (DE-588)4040936-3 (DE-588)4560677-8 |
| title | Pattern Classification Neuro-fuzzy Methods and Their Comparison |
| title_auth | Pattern Classification Neuro-fuzzy Methods and Their Comparison |
| title_exact_search | Pattern Classification Neuro-fuzzy Methods and Their Comparison |
| title_exact_search_txtP | Pattern Classification Neuro-fuzzy Methods and Their Comparison |
| title_full | Pattern Classification Neuro-fuzzy Methods and Their Comparison by Shigeo Abe |
| title_fullStr | Pattern Classification Neuro-fuzzy Methods and Their Comparison by Shigeo Abe |
| title_full_unstemmed | Pattern Classification Neuro-fuzzy Methods and Their Comparison by Shigeo Abe |
| title_short | Pattern Classification |
| title_sort | pattern classification neuro fuzzy methods and their comparison |
| title_sub | Neuro-fuzzy Methods and Their Comparison |
| topic | Artificial Intelligence Complexity Pattern Recognition Artificial intelligence Computational complexity Pattern recognition Mustererkennung (DE-588)4040936-3 gnd Neuro-Fuzzy-System (DE-588)4560677-8 gnd |
| topic_facet | Artificial Intelligence Complexity Pattern Recognition Artificial intelligence Computational complexity Pattern recognition Mustererkennung Neuro-Fuzzy-System |
| url | https://doi.org/10.1007/978-1-4471-0285-4 |
| work_keys_str_mv | AT abeshigeo patternclassificationneurofuzzymethodsandtheircomparison |