| 书目名称 | Pattern Classification | | 副标题 | Neuro-fuzzy Methods | | 编辑 | Shigeo Abe | | 视频video | | | 概述 | The unified approach for extracting fuzzy rules against different fuzzy classifier architectures.A new learning paradigm for neural network classifiers based on the network synthesis principle.Extensi | | 图书封面 |  | | 描述 | 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 | | 出版日期 | Book 2001 | | 关键词 | Fuzzy; Fuzzy function approximation; Pattern classification; Performance; Support Vector Machine; algorit | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4471-0285-4 | | isbn_softcover | 978-1-4471-1077-4 | | isbn_ebook | 978-1-4471-0285-4 | | copyright | Springer-Verlag London 2001 |
The information of publication is updating
|
|
发表于 2025-3-21 16:31:55
