Pattern Classification
Pattern Classification
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DOI：
10.1007/978-1-4471-0285-4
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发表时间：
2012-10
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影响因子：
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通讯作者：
Shigeo Abe DrEng
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文献类型：
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作者：
Shigeo Abe DrEng
神经网络具有学习能力，但对训练好的网络进行分析是困难的。另一方面，模糊规则的提取是困难的，但是一旦它们被提取出来，分析模糊系统就相对容易了。这本书通过开发神经网络和模糊系统的新的学习范例和架构来解决上述问题。本书由两部分组成：模式分类和函数逼近。在第一部分中，基于神经网络分类器的综合原理：讨论了一种新的学习范式，并对几种真实数据集的分类性能和训练时间与广泛使用的反向传播算法进行了比较;基于模糊规则的不同结构的模糊分类器可以定义为超盒，多面体或椭球区域。这本书讨论了训练这些模糊分类器的统一方法;使用几个真实世界的数据集评估了新开发的模糊分类器和传统分类器（如最近邻分类器和支持向量机）的性能，并阐明了它们的优缺点。第二部分：在第一部分讨论的基础上，讨论了函数逼近问题，并比较了函数逼近器的性能。本书主要面向人工智能和神经网络领域的研究人员和从业人员。
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.