An Information-Theoretic Approach to Neural Computing
An Information-Theoretic Approach to Neural Computing
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DOI：
10.1007/978-1-4612-4016-7
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发表时间：
1997-09
期刊：
影响因子：
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通讯作者：
G. Deco;D. Obradovic
中科院分区：
文献类型：
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作者：
G. Deco;D. Obradovic
神经网络为非线性复杂系统的建模和控制提供了一种强有力的新技术。在这本书中，作者从信息论的观点提出了神经网络的详细公式。他们展示了这种观点如何为神经网络的设计理论提供了新的见解。特别是，他们展示了如何将这些方法应用于监督和非监督学习的主题，包括特征提取、线性和非线性独立分量分析以及Boltzmann机器。假定读者对神经网络有一个基本的了解，但所有来自信息论的相关概念都被仔细地介绍和解释。因此，来自几个不同科学学科的读者，特别是认知科学家、工程师、物理学家、统计学家和计算机科学家，会发现这是对这个主题非常有价值的介绍。
Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint. They show how this perspective provides new insights into the design theory of neural networks. In particular they show how these methods may be applied to the topics of supervised and unsupervised learning including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from several different scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this to be a very valuable introduction to this topic.