一种新的聚类加权建模方法

IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222) Pub Date : 2001-07-15 DOI:10.1109/IJCNN.2001.938412

D. V. Prokhorov, L. Feldkamp, T. Feldkamp

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引用次数: 20

摘要

我们讨论了一种称为聚类加权建模(CWM)的联合密度估计方法。基本方法最初是由Gershenfeld(1998)提出的。我们描述了基础CWM的两个创新。其中，第一个特性使CWM能够处理连续的数据流。第二部分解决了在CWM参数调整过程中可能遇到的常见的局部极小值问题。我们缓解这个问题的方法非常复杂，但它代表了提高参数调整过程效率的原则方法。我们通过一个示例说明CWM和我们的性能增强。

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A new approach to cluster-weighted modeling

We discuss an approach to joint density estimation called cluster-weighted modeling (CWM). The base approach was originally proposed by Gershenfeld (1998). We describe two innovations to the base CWM. Among these, the first enables the CWM to work with continuous streams of data. The second addresses the commonplace problem of local minima which may be encountered during the CWM parameter adjustment process. Our approach to mitigate this problem is quite elaborate, but it represents a principled way of improving the efficacy of the parameter adjustment process. We illustrate CWM and our performance enhancements with an example.

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IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222)

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