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  1. NIIテクニカル・レポート

NII Technical Report (NII-2009-016E):Adaptive Classification Using Shared-Neighbor Information

https://doi.org/10.20736/0000001261
https://doi.org/10.20736/0000001261
cba256f9-859a-4465-948a-a4eb00e3967a
名前 / ファイル ライセンス アクション
09-016E.pdf NII Technical Report (NII-2009-016E):Adaptive Classification Using Shared-Neighbor Information (336.1 kB)
Item type レポート / Report(1)
公開日 2019-03-12
タイトル
言語 en
タイトル NII Technical Report (NII-2009-016E):Adaptive Classification Using Shared-Neighbor Information
言語
言語 eng
キーワード
言語 ja
主題Scheme Other
主題 テクニカルレポート
キーワード
言語 en
主題Scheme Other
主題 Technical Report
資源タイプ
資源 http://purl.org/coar/resource_type/c_6501
タイプ departmental bulletin paper
ID登録
ID登録 10.20736/0000001261
ID登録タイプ JaLC
著者 Houle, Michael E.

× Houle, Michael E.

en Houle, Michael E.

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Nett, Michael

× Nett, Michael

en Nett, Michael

Search repository
抄録
内容記述タイプ Abstract
内容記述 Nearest-neighbor approaches for classification have long been recognized for their potential in achieving low error rates, despite their perceived lack of scalability. Recent advances in the efficient computation of approximate k-nearest neighborhoods have made the nearest neighbor approaches more affordable in practice. However, their effectiveness is still limited due to their sensitivity to noise and to the choice of neighborhood size k. In this paper, we propose a general-purpose method for nearest-neighbor classification that seeks to compensate for the effects of noise through the determination of natural clusters in the vicinity of the test item. The classification model, based on elements of the relevant-set correlation (RSC) model for clustering, also allows for the automatic determination of an appropriate value of k for each test item. We also provide experimental results that demonstrate the competitiveness of our approach with that of other popular classification methods.
言語 en
書誌情報 ja : NIIテクニカル・レポート
en : NII Technical Report

p. 1-14, 発行日 2009-12-22
出版者
言語 ja
出版者 国立情報学研究所
ISSN
収録物識別子タイプ ISSN
収録物識別子 1346-5597
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