2026-03-06T21:39:14Z https://ir.soken.ac.jp/oai

oai:ir.soken.ac.jp:00003137 2023-06-20T15:37:50Z 2:429:19

サポートベクターマシンを用いた対話的文書検索 村田, 博士 ムラタ, ヒロシ MURATA, Hiroshi 総合研究大学院大学 博士（情報学）   We propose a heuristics which improves learning efficiency and retrieval efficiency in interactive document retrieval for selection of displayed doc- uments to a user. This heuristics is based on the extreme bias between positive and negative example.   We conducted experiments to evaluate the effectiveness of our proposed heuristics for active learning. We use a set of articles which is widely used in the text retrieval conference TREC. For comparison with our approach, two information retrieval methods were adopted. The first is conventional Rocchio-based relevance feedback. The second is conventional selection rule for SVM-based active learning. Then we confirmed our proposed system outperformed other ones.   Ordering of displayed documents is accomplished by calculation of the degree of relevance in interactive document retrieval. In SVM-based inter- active document retrieval, the degree of relevance is evaluated by signed distance from optimal hyperplane. It is not made clear how the signed distance on the SVMs has characteristics in Vector Space Model which is used in Rocchio-based method. We show that SVM-based retrieval has an association with conventional Rocchio-based method by comparative analysis of relevance evaluation.   As a result of their analysis, equation of weight vector of relevance feedback based on SVMs is equivalent to update equation of query vector of Rocchio-based method. The degree of relevance on SVM based method evaluates scalar product of norm of target document vector and cosine similarity of weight vector. On the other hand, the degree of relevance on Rocchio-based method evaluates cosine similarity of query vector.   From this knowledge, we propose a cosine kernel equivalent to cosine similarity that is suitable for SVM-based interactive document retrieval. The effectiveness of a method using our proposed cosine kernel was con- firmed, and it was experimentally compared with conventional relevance feedback for the Boolean, term frequency (TF) and term frequency- inverse document frequency (TFIDF) representations of document vec- tors. The experimental results for a Text Retrieval Conference data set show that the cosine kernel is effective for all document representations, especially TF representation. 総研大甲第1510号 thesis 2012-03-23 application/pdf application/pdf https://ir.soken.ac.jp/record/3137/files/甲1510_要旨.pdf https://ir.soken.ac.jp/record/3137/files/甲1510_本文.pdf https://ir.soken.ac.jp/records/3137 jpn