Machine learning by a subset of hypotheses
Takafumi Mukouchi, Tomoko K. MATSUSHIMA, Shigeichi Hirasawa · 2002
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.