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: | 1-十月-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 |
顯示於類別: | 會議論文 |