A self-growing probabilistic decision-based neural network with automatic data clustering

C. L. Tseng*, Y. H. Chen, Y. Y. Xu, Hsiao-Tien Pao, Hsin Chia Fu

*此作品的通信作者

研究成果: Article同行評審

12 引文 斯高帕斯(Scopus)

摘要

In this paper, we propose a new clustering algorithm for a mixture of Gaussian-based neural network and self-growing probabilistic decision-based neural networks (SPDNN). The proposed self-growing cluster learning (SGCL) algorithm is able to find the natural number of prototypes based on a self-growing validity measure, Bayesian information criterion (BIC). The learning process starts from a single prototype randomly initialized in the feature space and grows adaptively during the learning process until most appropriate number of prototypes are found. We have conducted numerical and real-world experiments to demonstrate the effectiveness of the SGCL algorithm. In the results of using SGCL to train the SPDNN for data clustering and speaker identification problems, we have observed a noticeable improvement among various model-based or vector quantization-based classification schemes.

原文American English
頁(從 - 到)21-38
頁數18
期刊Neurocomputing
61
發行號1-4
DOIs
出版狀態Published - 1 10月 2004

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