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Daisuke Ikeda

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AAAI Conference 2008 Conference Paper

Semi-Supervised Learning for Blog Classification

Daisuke Ikeda

Blog classification (e. g. , identifying bloggers’ gender or age) is one of the most interesting current problems in blog analysis. Although this problem is usually solved by applying supervised learning techniques, the large labeled dataset required for training is not always available. In contrast, unlabeled blogs can easily be collected from the web. Therefore, a semi-supervised learning method for blog classification, effectively using unlabeled data, is proposed. In this method, entries from the same blog are assumed to have the same characteristics. With this assumption, the proposed method captures the characteristics of each blog, such as writing style and topic, and uses these characteristics to improve the classification accuracy.

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TCS Journal 2000 Journal Article

Intractability of decision problems for finite-memory automata

Hiroshi Sakamoto
Daisuke Ikeda

This paper deals with finite-memory automata, introduced in Kaminski and Francez (Theoret. Comput. Sci. 134 (1994) 329–363). With a restricted memory structure that consists of a finite number of registers, a finite-memory automaton can store arbitrary input symbols. Thus, the language accepted by a finite-memory automaton is defined over a potentially infinite alphabet. The following decision problems are studied for a general finite-memory automata A as well as for deterministic ones: the membership problem, i. e. , given an A and a string w, to decide whether w is accepted by A, and the non-emptiness problem, i. e. , given an A, to decide whether the language accepted by A is non-empty. The membership problem is P-complete, provided a given automaton is deterministic, and each of the other problems is NP-complete. Thus, we conclude that the decision problems considered are intractable.

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