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无监督学习

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无监督学习(英语:unsupervised learning),又称非监督式学习,是机器学习的一种方法,没有给定事先标记过的训练示例,自动对输入的资料进行分类或分群。无监督学习的主要运用包含:聚类分析(cluster analysis)、关系规则(association rule)、维度缩减(dimensionality reduce)。它是监督式学习强化学习等策略之外的一种选择。

一个常见的无监督学习是数据聚类。在人工神經网络中,生成对抗网络(GAN)、自组织映射(SOM)和适应性共振理论(ART)则是最常用的非监督式学习。

ART模型允许集群的个数可随着问题的大小而变动,并让用户控制成员和同一个集群之间的相似度分数,其方式为透过一个由用户自定而被称为警觉参数的常量。ART也用于模式识别,如自动目标识别和数字信号处理。第一个版本为"ART1",是由卡本特和葛罗斯柏格所发展的。

方法

非监督式学习常使用的方法有很多种,包括:

另见

参考文献

  • Geoffrey Hinton, Terrence J. Sejnowski(editors,1999) Unsupervised Learning and Map Formation: Foundations of Neural Computation, MIT Press, ISBN 0-262-58168-X(这本书专注于人工神經网络的非监督式学习)
  • S. Kotsiantis, P. Pintelas, Recent Advances in Clustering: A Brief Survey, WSEAS Transactions on Information Science and Applications, Vol 1, No 1 (73-81), 2004.
  • Richard O. Duda, Peter E. Hart, David G. Stork. Unsupervised Learning and Clustering, Ch. 10 in Pattern classification (2nd edition), p. 571, Wiley, New York, ISBN 0-471-05669-3, 2001.
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无监督学习
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