講演名 2005-12-16
Adaptive Subspace Splitting Using Minimum-Description-Length-Principles for Efficient Boosting
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抄録(和)
抄録(英) In RealBoost learning, selecting best weak classifiers is one of the most significant tasks. Generally, it is done according to its discriminant power which are normally measured by dissimilarity of distributions of positive and negative samples such as Bhattacharyya distance, Kullback-Leibler divergence, the recent Jensen-Shannon divergence and Informax. These distributions are often estimated through splitting the range of continuous feature values into a predefined number of equal-width intervals and then computing histograms. So far, choosing the most appropriate number of intervals is still a challenging task because a small number of intervals might not well approximate the real distribution while a large number of intervals might cause over-fitting, increase computation time and waste storage space. Therefore, this paper proposes using Minimum-Description-Length-Principles (MDLP) based discretization method for automatically and optimally choosing it. Experiments on the integrating MDLP-based subspace splitting into RealBoost have shown that strong classifiers learned by the proposed method can achieve stable performance, avoid over-fitting and have compact storage space.
キーワード(和)
キーワード(英) AdaBoost / MDLP based discretization / object detection
資料番号 PRMU2005-130
発行日

研究会情報
研究会 PRMU
開催期間 2005/12/9(から1日開催)
開催地(和)
開催地(英)
テーマ(和)
テーマ(英)
委員長氏名(和)
委員長氏名(英)
副委員長氏名(和)
副委員長氏名(英)
幹事氏名(和)
幹事氏名(英)
幹事補佐氏名(和)
幹事補佐氏名(英)

講演論文情報詳細
申込み研究会 Pattern Recognition and Media Understanding (PRMU)
本文の言語 ENG
タイトル(和)
サブタイトル(和)
タイトル(英) Adaptive Subspace Splitting Using Minimum-Description-Length-Principles for Efficient Boosting
サブタイトル(和)
キーワード(1)(和/英) / AdaBoost
第 1 著者 氏名(和/英) / Duy Dinh LE
第 1 著者 所属(和/英)
Department of Informatics, The Graduate University for Advanced Studies
発表年月日 2005-12-16
資料番号 PRMU2005-130
巻番号(vol) vol.105
号番号(no) 478
ページ範囲 pp.-
ページ数 6
発行日