標題: A self-growing probabilistic decision-based neural network with automatic data clustering
作者: Tseng, CL
Chen, YH
Xu, YY
Pao, HT
Fu, HC
資訊工程學系
管理科學系
Department of Computer Science
Department of Management Science
關鍵字: self-growing probabilistic decision-based neural networks (SPDNN);supervised learning;automatic data clustering;validity measure;Bayesian information criterion
公開日期: 1-Oct-2004
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. (C) 2004 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.neucom.2004.03.002
http://hdl.handle.net/11536/26351
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2004.03.002
期刊: NEUROCOMPUTING
Volume: 61
Issue: 
起始頁: 21
結束頁: 38
Appears in Collections:Conferences Paper


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