Extraction of Frequent Sub-Sequences from Long Sequence Using Neural Network
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- MORITA Kenta
- Graduate School of Engineering, Mie University
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- TAKASE Haruhiko
- Graduate School of Engineering, Mie University
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- KAWANAKA Hiroharu
- Graduate School of Engineering, Mie University
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- MORITA Naoki
- Graduate School of Information and Telecommunication Engineering, Tokai University
Bibliographic Information
- Other Title
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- 順序列から任意の出現頻度以上の部分列を抽出するニューラルネットワーク
- ジュンジョレツ カラ ニンイ ノ シュツゲン ヒンド イジョウ ノ ブブンレツ オ チュウシュツ スル ニューラルネットワーク
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Abstract
<p>In this paper, we aim to extract frequent sub-sequences from a given long sequence. Especially, the method satisfies following four requirements: (1) online learning, (2) extracting plural sub-sequences, (3) extracting various length sub-sequences, and (4) controlling threshold related to frequency. The proposed method uses a 2-blocks neural network. The network consists of spiking neurons based on leaky integrate and fire (LIF) model and is trained by the method based on spike timing dependency plasticity (STDP). As a result, the network extracted sub-sequences whose frequency is more than a certain threshold that is determined by only one parameter. Concretely, the network extracted three-symbols-length subsequences from 3,000 length sequence. In this case, sub-sequences appeared frequency of 0.4%, 3%, or 5%, and the network extracted 3%-and-more sub-sequences, or only 5% sub-sequences by controlling only one parameter.</p>
Journal
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- Journal of Japan Society for Fuzzy Theory and Intelligent Informatics
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Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 31 (1), 592-596, 2019-02-15
Japan Society for Fuzzy Theory and Intelligent Informatics
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Details 詳細情報について
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- CRID
- 1390564238076881536
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- NII Article ID
- 130007594492
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- NII Book ID
- AA1181479X
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- ISSN
- 18817203
- 13477986
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- NDL BIB ID
- 029528964
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed