Title :
Bayesian Ying-Yang harmony learning, information theory and information geometry
Author_Institution :
Dept. of Comput. Sci., Chinese Univ. of Hong Kong, Shatin, China
Abstract :
Learning tasks are viewed from the fundamental relationships among the observed data. The key issues of Bayesian Ying Yang system and harmony learning theory are summarized from a systematical perspective. Further discussions are made on its relations to maximum likelihood learning, information theory, information geometry and Helmholtz machine learning
Keywords :
Bayes methods; learning (artificial intelligence); maximum likelihood estimation; neural nets; Bayesian Ying-Yang harmony learning; Helmholtz machine learning; information geometry; information theory; maximum likelihood learning; neural nets; Bayesian methods; Computer science; Covariance matrix; Information geometry; Information theory; Lattices; Machine learning; Maximum likelihood estimation; Stochastic processes; Topology;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.938807