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Akira Maeda

Researcher at Ritsumeikan University

Publications -  95
Citations -  459

Akira Maeda is an academic researcher from Ritsumeikan University. The author has contributed to research in topics: Metadata & Cross-language information retrieval. The author has an hindex of 9, co-authored 92 publications receiving 443 citations. Previous affiliations of Akira Maeda include Nara Institute of Science and Technology.

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Proceedings ArticleDOI

Unsupervised Outlier Detection in Time Series Data

TL;DR: It is observed PGA can detect those brokers who suddenly start selling the stock in a different way to other brokers to whom they were previously similar, and applied t-statistics to find the deviations effectively.
Proceedings ArticleDOI

Query term disambiguation for Web cross-language information retrieval using a search engine

TL;DR: This paper proposes a disambiguation method for dictionary-based query translation that is independent of the availability of such scarce language resources, while achieving adequate retrieval effectiveness by utilizing Web documents as a corpus and using co-occurrence information between terms within that corpus.
Proceedings ArticleDOI

A browsing tool of multi-lingual documents for users without multi-lingual fonts

TL;DR: A multilingual document browsing tool for a user with no multilingual fonts on his or her terminal is presented and a browser which sends a text string with the font glyphs required to display the text is proposed.
Journal ArticleDOI

Viewing multilingual documents on your local Web browser

TL;DR: The developed a technology called MHTML to browse multilingual documents on an off-the-shelf Web browser, and applied the technology to a multilingual gateway service to browse foreign documents and to aMultilingual electronic text collection of Japanese folk tales.

Anomaly Detection Using Unsupervised Profiling Method in Time Series Data.

TL;DR: The experimental results demonstrate that the PGA method is able to flag anomalous records effectively and to characterize the expected pattern of behavior around the target sequence in terms of the behavior of similar objects.