Please use this identifier to cite or link to this item:
Title: Entropy penalized learning for gaussian mixture models
Authors: Wang, Bo Yu
Wan, Feng
Mak, Peng Un (麥炳源)
Vai, Mang I (韋孟宇)
Mak, Pui In (麥沛然)
Issue Date: Aug-2011
Publisher: IEEE
Citation: The international joint conference on neural networks (IJCNN 2011), Aug. 2011, p. 2067-2073
Abstract: In this paper, we propose an entropy penalized approach to address the problem of learning the parameters of Gaussian mixture models (GMMs) with components of small weights. In addition, since the method is based on minimum message length (MML) criterion, it can also determine the number of components of the mixture model. The simulation results demonstrate that our method outperform several other state-of-art model selection algorithms especially for the mixtures with components of very different weights.
ISSN: 2161-4393
Keywords: Gaussian processes
Learning (artificial intelligence)
Access: View full-text via DOI

Files in This Item:
File Description SizeFormat 
7438_0_IJCNN.pdf401.77 kBAdobe PDFView/Open
Appears in Collections:ECE Conference Papers

Items in UMIR are protected by copyright, with all rights reserved, unless otherwise indicated.