{"created":"2025-01-18T23:00:58.216722+00:00","updated":"2025-01-22T16:31:39.388587+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00031696","sets":["1164:2592:2600:2606"]},"path":["2606"],"owner":"1","recid":"31696","title":["ベイズ符号化アルゴリズムを用いたテキストデータ圧縮"],"pubdate":{"attribute_name":"公開日","attribute_value":"2007-01-23"},"_buckets":{"deposit":"023fdae1-abc5-4549-9051-d8cd36dff121"},"_deposit":{"id":"31696","pid":{"type":"depid","value":"31696","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"ベイズ符号化アルゴリズムを用いたテキストデータ圧縮","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ベイズ符号化アルゴリズムを用いたテキストデータ圧縮"},{"subitem_title":"Text Data Compression by Bayes Coding Algorithm","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2007-01-23","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"早稲田大学理工学部経営システムエ学科"},{"subitem_text_value":"早稲田大学理工学部経営システムエ学科"},{"subitem_text_value":"早稲田大学理工学部経営システムエ学科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Industrial Management System Engineering, School of Science and Engineering, Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Department of Industrial Management System Engineering, School of Science and Engineering, Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Department of Industrial Management System Engineering, School of Science and Engineering, Waseda University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/31696/files/IPSJ-AL07110003.pdf"},"date":[{"dateType":"Available","dateValue":"2009-01-23"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-AL07110003.pdf","filesize":[{"value":"582.4 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"9"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"91d68c4f-300b-45bf-84af-79ac9ac71857","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2007 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"中野, 晶"},{"creatorName":"小泉, 大城"},{"creatorName":"松嶋, 敏泰"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Akira, Nakano","creatorNameLang":"en"},{"creatorName":"Daiki, Koizumi","creatorNameLang":"en"},{"creatorName":"Toshiyasu, Matsushima","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN1009593X","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"ベイズ符号は，情報源の確率分布のクラスは既知であり，そのパラメータは未知である場合のユニバーサル情報源符号化法で，ベイズ基準の下で冗長度を最小にする符号である．Contextnee情報源に対してベイズ符号を実現する手法として，逐次型ベイズ符号化アルゴリズムがあり，その必要メモリ量を削減したアルゴリズムとして改良型ベイズ符号化アルゴリズムがある．これらを用いてテキストデータを圧縮した際，モデルやパラメータの事前分布の不適合性より既存の圧縮ソフトウェアのbzip2などに圧縮性能で劣ることや，圧縮時の必要メモリ量が莫大なことなどから実用化には至っていない本研究では，ベイズ符号の実用化に向けて，必要メモリ量に制約をおいた上で，テキストデータに適合する事前分布を導入することにより圧縮性能を向上させることを目的とする．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Bayes code is one of universal source coding algorithms, such that a class of the probabilistic model of source is known but its parameter is unknown. Bayes code provides Bayes optimality in terms of the redundancy. Bayes coding algorithm for the context tree sources has been proposed, and modified version of this algorithm to reduce the required memory has also been proposed. When text data is compressed by these algorithms, however, there are two problems. One is that the compression ratio is worse than conventional data compression algorithm such as bzip2, because the prior distribution of model or parameter does not fit text data. The other is that it needs enormous memory under the compression. This paper tries to improve Bayes coding algorithm in term of the compression ratio by using the other prior distribution, as well as constraint the limit of memory requirement, toward the implementation of text compression by Bayes coding algorithm. ","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"22","bibliographic_titles":[{"bibliographic_title":"情報処理学会研究報告アルゴリズム（AL）"}],"bibliographicPageStart":"15","bibliographicIssueDates":{"bibliographicIssueDate":"2007-01-23","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5(2007-AL-110)","bibliographicVolumeNumber":"2007"}]},"relation_version_is_last":true,"weko_creator_id":"1"},"id":31696,"links":{}}