Title :
Bayesian Signal Classifier
Author :
Chow, Chi Kin ; Yuen, Shiu Yin
Author_Institution :
City Univ. of Hong Kong, Kowloon
Abstract :
This article points out the limitations of vectoral input pattern on density estimation and Bayesian classification. A continuous Bayesian classifier is proposed to tackle these limitations. The classifier accepts signal as input pattern; thus the problem of optimal description length selection is avoided. The algorithm is evaluated on signal clustering and distribution classification.
Keywords :
Bayes methods; pattern clustering; signal classification; statistical distributions; Bayesian signal classifier; density estimation; distribution classification; optimal description length selection; signal clustering; vectoral input pattern; Bayesian methods; Classification algorithms; Clustering algorithms; Costs; Distributed computing; Feature extraction; Information geometry; Nearest neighbor searches; Neural networks; Spline;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4370956
