{"_buckets": {"deposit": "8a0d946b-0465-40d8-9a4b-05ce260e7b9b"}, "_deposit": {"created_by": 21, "id": "3137", "owners": [21], "pid": {"revision_id": 0, "type": "depid", "value": "3137"}, "status": "published"}, "_oai": {"id": "oai:ir.soken.ac.jp:00003137", "sets": ["19"]}, "author_link": ["208", "209", "207"], "item_1_biblio_info_21": {"attribute_name": "書誌情報（ソート用）", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2012-03-23", "bibliographicIssueDateType": "Issued"}, "bibliographic_titles": [{}]}]}, "item_1_creator_2": {"attribute_name": "著者名", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "村田, 博士"}], "nameIdentifiers": [{"nameIdentifier": "207", "nameIdentifierScheme": "WEKO"}]}]}, "item_1_creator_3": {"attribute_name": "フリガナ", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "ムラタ, ヒロシ"}], "nameIdentifiers": [{"nameIdentifier": "208", "nameIdentifierScheme": "WEKO"}]}]}, "item_1_date_granted_11": {"attribute_name": "学位授与年月日", "attribute_value_mlt": [{"subitem_dategranted": "2012-03-23"}]}, "item_1_degree_grantor_5": {"attribute_name": "学位授与機関", "attribute_value_mlt": [{"subitem_degreegrantor": [{"subitem_degreegrantor_name": "総合研究大学院大学"}]}]}, "item_1_degree_name_6": {"attribute_name": "学位名", "attribute_value_mlt": [{"subitem_degreename": "博士（情報学）"}]}, "item_1_description_1": {"attribute_name": "ID", "attribute_value_mlt": [{"subitem_description": "2012045", "subitem_description_type": "Other"}]}, "item_1_description_12": {"attribute_name": "要旨", "attribute_value_mlt": [{"subitem_description": "\u0026nbsp;\u0026nbsp;We propose a heuristics which improves learning efficiency and retrieval\r\nefficiency in interactive document retrieval for selection of displayed doc-\r\numents to a user. This heuristics is based on the extreme bias between\r\npositive and negative example.\r\n\u0026nbsp;\u0026nbsp;We conducted experiments to evaluate the effectiveness of our proposed\r\nheuristics for active learning. We use a set of articles which is widely used\r\nin the text retrieval conference TREC. For comparison with our approach,\r\ntwo information retrieval methods were adopted. The first is conventional\r\nRocchio-based relevance feedback. The second is conventional selection\r\nrule for SVM-based active learning. Then we confirmed our proposed\r\nsystem outperformed other ones.\r\n\u0026nbsp;\u0026nbsp;Ordering of displayed documents is accomplished by calculation of the\r\ndegree of relevance in interactive document retrieval. In SVM-based inter-\r\nactive document retrieval, the degree of relevance is evaluated by signed\r\ndistance from optimal hyperplane. It is not made clear how the signed\r\ndistance on the SVMs has characteristics in Vector Space Model which is\r\nused in Rocchio-based method. We show that SVM-based retrieval has\r\nan association with conventional Rocchio-based method by comparative\r\nanalysis of relevance evaluation.\r\n\u0026nbsp;\u0026nbsp;As a result of their analysis, equation of weight vector of relevance\r\nfeedback based on SVMs is equivalent to update equation of query vector\r\nof Rocchio-based method. The degree of relevance on SVM based method\r\nevaluates scalar product of norm of target document vector and cosine\r\nsimilarity of weight vector. On the other hand, the degree of relevance\r\non Rocchio-based method evaluates cosine similarity of query vector.\r\n\u0026nbsp;\u0026nbsp;From this knowledge, we propose a cosine kernel equivalent to cosine\r\nsimilarity that is suitable for SVM-based interactive document retrieval.\r\nThe effectiveness of a method using our proposed cosine kernel was con-\r\nfirmed, and it was experimentally compared with conventional relevance\r\nfeedback for the Boolean, term frequency (TF) and term frequency-\r\ninverse document frequency (TFIDF) representations of document vec-\r\ntors. The experimental results for a Text Retrieval Conference data set\r\nshow that the cosine kernel is effective for all document representations,\r\nespecially TF representation.", "subitem_description_type": "Other"}]}, "item_1_description_7": {"attribute_name": "学位記番号", "attribute_value_mlt": [{"subitem_description": "総研大甲第1510号", "subitem_description_type": "Other"}]}, "item_1_select_14": {"attribute_name": "所蔵", "attribute_value_mlt": [{"subitem_select_item": "有"}]}, "item_1_select_16": {"attribute_name": "複写", "attribute_value_mlt": [{"subitem_select_item": "全文公開可"}]}, "item_1_select_17": {"attribute_name": "公開状況", "attribute_value_mlt": [{"subitem_select_item": "application/pdf"}]}, "item_1_select_8": {"attribute_name": "研究科", "attribute_value_mlt": [{"subitem_select_item": "複合科学研究科"}]}, "item_1_select_9": {"attribute_name": "専攻", "attribute_value_mlt": [{"subitem_select_item": "17 情報学専攻"}]}, "item_1_text_10": {"attribute_name": "学位授与年度", "attribute_value_mlt": [{"subitem_text_value": "2011"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "MURATA, Hiroshi", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "209", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2016-02-17"}], "displaytype": "simple", "download_preview_message": "", "file_order": 0, "filename": "甲1510_要旨.pdf", "filesize": [{"value": "312.6 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_11", "mimetype": "application/pdf", "size": 312600.0, "url": {"label": "要旨・審査要旨", "url": "https://ir.soken.ac.jp/record/3137/files/甲1510_要旨.pdf"}, "version_id": "038d078c-5b01-4c7d-9e46-61f61243baa0"}, {"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2016-02-17"}], "displaytype": "simple", "download_preview_message": "", "file_order": 1, "filename": "甲1510_本文.pdf", "filesize": [{"value": "1.9 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_11", "mimetype": "application/pdf", "size": 1900000.0, "url": {"label": "本文", "url": "https://ir.soken.ac.jp/record/3137/files/甲1510_本文.pdf"}, "version_id": "911217e8-b61e-40d2-b6b2-93970b93265d"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "jpn"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "thesis", "resourceuri": "http://purl.org/coar/resource_type/c_46ec"}]}, "item_title": "サポートベクターマシンを用いた対話的文書検索", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "サポートベクターマシンを用いた対話的文書検索"}]}, "item_type_id": "1", "owner": "21", "path": ["19"], "permalink_uri": "https://ir.soken.ac.jp/records/3137", "pubdate": {"attribute_name": "公開日", "attribute_value": "2012-09-13"}, "publish_date": "2012-09-13", "publish_status": "0", "recid": "3137", "relation": {}, "relation_version_is_last": true, "title": ["サポートベクターマシンを用いた対話的文書検索"], "weko_shared_id": -1}