Title :
Machine learning by a subset of hypotheses
Author :
Mukouchi, Takafumi ; Matsushima, Toshiyasu ; Hirasawa, Shigeichi
Author_Institution :
NTT Commun. Sci. Labs., Yokosuka, Japan
Abstract :
Bayesian theory is effective in statistics, lossless source coding, machine learning, etc. It is often, however, computationally expensive since the calculation of posterior probabilities and of mixture distributions is not tractable. In this paper, we propose a new method for approximately calculating mixture distributions in a discrete hypothesis class
Keywords :
Bayes methods; decision theory; learning systems; maximum likelihood estimation; minimisation; probability; stochastic processes; Bayes method; decision theory; hypotheses; machine learning; minimisation; mixture distributions; posterior probability; probability; stochastic learning; Bayesian methods; Communication industry; Distributed computing; Engineering management; Machine learning; Probability; Source coding; Statistical distributions; Stochastic processes; Systems engineering and theory;
Conference_Titel :
Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-4053-1
DOI :
10.1109/ICSMC.1997.635315
