Presentation 2011-03-29
Sequence Image Retrieval by Combining a Meta-Learing Algorithm and Query Learning
Ryoichi HATA, Madori IKEDA, Mahito SUGIYAMA, Akihiro YAMAMOTO,
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Abstract(in English) In this paper we propose a method combining query learning and a meta-algorithm of machine learning as a new approach for sequence image retrieval. Sequence image retrieval is technology for searching image of character sequences without character recognition. The query learning mechanism is introduced to our method to use actively sequence images which are outputs in iteration of search. A new algorithm based on a meta-algorithm is also introduced. The algorithm adopts the weighted averages to amalgamate outputs of learning machines. We use document images which are obtained by artificially deteriorating original printed images, and as a result we found our method enhanced precision with using the query learning one-time. In addition, we check by experiment with pseudo data to see that our algorithm is applicable to other learning problems.
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Keyword(in English) Sequence image retrieval / Query learning / Meta-Algorithm
Paper # IBISML2010-120
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Conference Information
Committee IBISML
Conference Date 2011/3/21(1days)
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Paper Information
Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Sequence Image Retrieval by Combining a Meta-Learing Algorithm and Query Learning
Sub Title (in English)
Keyword(1) Sequence image retrieval
Keyword(2) Query learning
Keyword(3) Meta-Algorithm
1st Author's Name Ryoichi HATA
1st Author's Affiliation Graduate School of Informatics, Kyoto University()
2nd Author's Name Madori IKEDA
2nd Author's Affiliation Graduate School of Informatics, Kyoto University
3rd Author's Name Mahito SUGIYAMA
3rd Author's Affiliation Graduate School of Informatics, Kyoto University
4th Author's Name Akihiro YAMAMOTO
4th Author's Affiliation Graduate School of Informatics, Kyoto University
Date 2011-03-29
Paper # IBISML2010-120
Volume (vol) vol.110
Number (no) 476
Page pp.pp.-
#Pages 8
Date of Issue