Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
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发表时间：
2001-03
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通讯作者：
V. Kecman
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作者：
V. Kecman
这本教科书提供了从实验数据和软计算学习领域的全面介绍。支持向量机（SVM）和神经网络（NN）是数学结构或模型，是学习的基础，而模糊逻辑系统（FLS）使我们能够将结构化的人类知识嵌入到可行的算法中。这本书假设它不仅是有用的，而且是必要的，将SVM，NN和FLS视为一个连接的整体的一部分。在整个过程中，理论和算法说明了实际的例子，以及问题集和模拟实验。这种方法使读者能够开发SVM，NN和FLS，除了理解它们。这本书还介绍了三个案例研究：基于NN的控制，金融时间序列分析和计算机图形。解决方案手册和模拟实验所需的所有MATLAB程序都可用。
This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.