適応的仮説選択による対話型概念学習

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The process of concept learning can be viewed as a search for general concept descriptions that cover all of the positive examples and none of the negative examples given by the teacher. In such learning, the examples give crucial influence on the efficiency of learning. However,it is not easy for the teacher to select good examples. On the other hand, the learner can select effective examples for verifying the candidates of the result, because he will always know the descriptions as the candidates. Accordingly, the way that the learner selects examples and questions whether each of them is positive or negative is of great importance. Such learning is called, in general, interactive concept learning. In interactive concept learning, there are two factors to realize the efficient learning. One is the number of questions, and the other is the cost for selecting an example. So far, there have been proposed various method of interactive concept learning. Halving guarantees to learn by the minimum number of questions, and it requires high cost for selecting an example. Conservative Selection (CS) requires low cost for selecting an example, and the number of questions increases in case that a lot of generalization is needed. Middle Hypothesis Selection (MHS), which we have proposed, requires low cost like CS, and the number of questions increases in case that the generalization tree has a large number of branches. In this paper, we propose a new interactive concept learning method, an improved version of MHS, named Adaptive Hypothesis Selection (AHS). AHS dynamically applies two ways for selecting examples by the criterion function, which determines relevant hypothesis selection based on the number of middle hypothesis and the depth of generalization tree. We discuss the efficiency of AHS compared with the existing methods and show that it can learn in less number of questions. And we experimentally verify the validity of the proposed criterion function.
社団法人人工知能学会の論文
1992-03-01

適応的仮説選択による対話型概念学習

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