贝叶斯信号分类器

2007 International Joint Conference on Neural Networks Pub Date : 2007-10-29 DOI:10.1109/IJCNN.2007.4370956

C. Chow, S. Y. Yuen

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引用次数: 0

摘要

本文指出了矢量输入模式在密度估计和贝叶斯分类上的局限性。提出了一种连续贝叶斯分类器来解决这些限制。分类器接受信号作为输入模式;从而避免了最优描述长度选择问题。对该算法进行了信号聚类和分布分类评价。

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Bayesian Signal Classifier

This article points out the limitations of vectoral input pattern on density estimation and Bayesian classification. A continuous Bayesian classifier is proposed to tackle these limitations. The classifier accepts signal as input pattern; thus the problem of optimal description length selection is avoided. The algorithm is evaluated on signal clustering and distribution classification.

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来源期刊

2007 International Joint Conference on Neural Networks

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