最小均方学习子空间信息准则的不敏感修正

2013 Sixth International Symposium on Computational Intelligence and Design Pub Date : 2013-10-28 DOI:10.1109/ISCID.2013.219

Xuejun Zhou

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引用次数: 0

摘要

最小均方差(LMS)算法在机器学习领域得到了广泛的应用。子空间信息准则的不敏感修正(IMSIC)是一种模型选择方法，它是在泛化误差的无偏估计量-子空间信息准则(SIC)上定义的。在本文中，我们将给出基于IMSIC的LMS学习模型的选择方法。

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Insensitive Modification of Subspace Information Criterion for Least Mean Squares Learning

The least mean squares (LMS) algorithm is widely applied in the machine learning community. Insensitive Modification of Subspace Information Criterion (IMSIC) is one of the model selection methods, which is defined on an unbiased estimator of the generalization error-Subspace Information Criterion(SIC). In this paper, we will give the method of selecting LMS learning models by IMSIC.

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2013 Sixth International Symposium on Computational Intelligence and Design

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