Neural networks
Neural networks
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DOI：
10.1002/wics.50
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
M. Titterington
中科院分区：
文献类型：
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作者：
M. Titterington
对多种人工神经网络进行了综述，包括前馈网络、递归网络、像霍普菲尔德网络这样的联想记忆网络以及自组织映射网络。描述了它们的架构以及为训练它们而开发的方法。特别强调了与统计活动的联系，尤其是在图形模型和潜在结构模型等背景下的回归、分类和聚类。在训练过程方面，关注了诸如似然法和贝叶斯方法等统计方法的隐式或显式实施。版权所有©2009约翰威立父子公司
A variety of artificial neural networks are reviewed, including feed‐forward networks, recurrent networks, associative memories such as the Hopfield network, and the self‐organizing map. Their architectures are described, as are methods that have been developed for training them. Particular emphasis is placed on links with statistical activities, especially regression, classification, and clustering, in contexts such as graphical models and latent structure models. In terms of the training procedures, attention is drawn to the implicit or explicit implementation of statistical methodological approaches such as likelihood and Bayesian methods. Copyright © 2009 John Wiley & Sons, Inc.