@inproceedings{718c28332a034b8fa51a1d36dbaa6cf8,
title = "Supervised Learning Using Characteristic Generalized Gaussian Density and Its Application to Chinese Materia Medica Identification",
abstract = "This paper presents the estimation of the characteristic generalized Gaussian density (CGGD) given a set of known GGD distributions based on some optimization techniques, and its application to the Chinese Materia Medica identification. The CGGD parameters are estimated by minimizing the distance between the CGGD distribution and known GGD distributions. Our experimental results show that the proposed signature based on the CGGD together with the use of Kullback-Leibler distance outperforms the traditional wavelet-based energy signature. The recognition rate for the proposed method is higher than the energy signature by at least 10% to around 60% – 70%. Nevertheless, the extraction of CGGD estimators still retains comparable level of computational complexity. In general, our proposed method is very competitive compared with many other existing Chinese Materia Medica classification methods.",
keywords = "Generalized Gaussian density, Kullback-Leibler distance, Similarity measurement, Supervised learning, Wavelets",
author = "Choy, {S. K.} and TONG, {Chong Sze}",
note = "Publisher Copyright: {\textcopyright} 2006 Birkh{\"a}user Verlag Basel/Switzerland. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; Wavelet Analysis and Applications 2005 ; Conference date: 29-11-2005 Through 02-12-2005",
year = "2007",
doi = "10.1007/978-3-7643-7778-6_32",
language = "English",
isbn = "9783764377779",
series = "Applied and Numerical Harmonic Analysis",
publisher = "Birkh{\"a}user Basel - Springer",
pages = "443--452",
editor = "Tao Qian and Vai, {Mang I.} and Yuesheng Xu",
booktitle = "Wavelet Analysis and Applications",
edition = "1st",
}