最尤法と無差別原理を用いた確率分布からの帰納学習

概要

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This paper presents an algorithm for unsupervised inductive learning from discrete probability distributions. Few algorithms have directly dealt with probability distributions. The procedures are as follows: 1. Find a probability distribution corresponding to a proposition of classical logic using maximum liklihood method. 2. Transform the probability distribution to a proposition based on the principle of indifference. 3. Reduce the proposition. The principle of indifference states that a probability distribution is uniform when we have no information. Using this principle, the propositions of classical logic can be corresponded to some probability distributions. The algorithm is applied to a real data. The result shows that the algorithm works well. The result is also compared with a method of multivariate analysis.
社団法人人工知能学会の論文
1997-03-01