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Classification of object sequences using syntactical structure. (English) Zbl 1052.68693

Arikawa, Setsuo (ed.) et al., Progress in discovery science. Final report of the Japanese discovery science project. Berlin: Springer (ISBN 3-540-43338-4). Lect. Notes Comput. Sci. 2281, 318-326 (2002).
Summary: When classifying a sequence of objects, in an ordinary classification, where objects are assumed to be independently drawn from identical information sources, each object is classified independently. This assumption often causes deterioration in the accuracy of classification. In this paper, we consider a method to classify objects in a sequence by taking account of the context of the sequence. We define this problem as component classification and present a dynamic programming algorithm where a hidden Markov model is used to describe the probability distribution of the object sequences. We show the effectiveness of the component classification experimentally, using musical structure analysis.
For the entire collection see [Zbl 0988.00034].

MSC:

68T05 Learning and adaptive systems in artificial intelligence
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