ログイン
言語:

WEKO3

  • トップ
  • ランキング
To
lat lon distance
To

Field does not validate



インデックスリンク

インデックスツリー

メールアドレスを入力してください。

WEKO

One fine body…

WEKO

One fine body…

アイテム

  1. 020 学位論文
  2. 複合科学研究科
  3. 17 情報学専攻

サポートベクターマシンを用いた対話的文書検索

https://ir.soken.ac.jp/records/3137
https://ir.soken.ac.jp/records/3137
665b966a-9193-4add-a91d-7265e6219015
名前 / ファイル ライセンス アクション
甲1510_要旨.pdf 要旨・審査要旨 (312.6 kB)
甲1510_本文.pdf 本文 (1.9 MB)
Item type 学位論文 / Thesis or Dissertation(1)
公開日 2012-09-13
タイトル
タイトル サポートベクターマシンを用いた対話的文書検索
言語
言語 jpn
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_46ec
資源タイプ thesis
著者名 村田, 博士

× 村田, 博士

村田, 博士

Search repository
フリガナ ムラタ, ヒロシ

× ムラタ, ヒロシ

ムラタ, ヒロシ

Search repository
著者 MURATA, Hiroshi

× MURATA, Hiroshi

en MURATA, Hiroshi

Search repository
学位授与機関
学位授与機関名 総合研究大学院大学
学位名
学位名 博士(情報学)
学位記番号
内容記述タイプ Other
内容記述 総研大甲第1510号
研究科
値 複合科学研究科
専攻
値 17 情報学専攻
学位授与年月日
学位授与年月日 2012-03-23
学位授与年度
値 2011
要旨
内容記述タイプ Other
内容記述   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.
所蔵
値 有
戻る
0
views
See details
Views

Versions

Ver.1 2023-06-20 15:37:48.851191
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Cite as

エクスポート

OAI-PMH
  • OAI-PMH JPCOAR 2.0
  • OAI-PMH JPCOAR 1.0
  • OAI-PMH DublinCore
  • OAI-PMH DDI
Other Formats
  • JSON
  • BIBTEX

Confirm


Powered by WEKO3


Powered by WEKO3