標題: A Self-growing Probabilistic decision-based neural network for anchor/speaker identification
作者: Chen, YH
Tseng, CL
Fu, HC
Pao, HT
資訊工程學系
管理科學系
Department of Computer Science
Department of Management Science
公開日期: 2003
摘要: In this paper, we propose a new clustering algorithm for a mixture Gaussian based neural network, called 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 with 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 conduct numerical and real world experiments to demostrate the effectiveness of the SGCL algorithm. In the results of using SGCL to trainin the SPDNN for anchor/speaker identification, we have observed noticeable improvement among various model-based or vector quantization-based classification schemes.
URI: http://hdl.handle.net/11536/28277
ISBN: 3-540-40408-2
ISSN: 0302-9743
期刊: ARTIFICAIL NEURAL NETWORKS AND NEURAL INFORMATION PROCESSING - ICAN/ICONIP 2003
Volume: 2714
起始頁: 686
結束頁: 694
Appears in Collections:Conferences Paper