A Local Learning Framework Based on Multiple Local Classifiers(Pattern Recognition)

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概要

• 論文の詳細を見る

• This paper presents a local learning framework in which the local classifiers can be pre-learned and the support size of each classifier can be selected to minimize the error bound. The proposed algorithm is compared with the conventional support vector machine (SVM). Experimental results show that our scheme using the user-defined parameters C and σ is more accurate and less sensitive than the conventional SVM.

• 社団法人電子情報通信学会の論文
• 2004-07-01

著者

• Kim Jongdae

  Division Of Information Engineering And Telecommunications Hallym University

• KIM BaekSop

  Division of Information Engineering and Telecommunications, Hallym University

• SONG Hyejeong

  Division of Information Engineering and Telecommunications, Hallym University

• Kim Baeksop

  Division Of Information Engineering And Telecommunications Hallym University

• Song Hyejeong

  Division Of Information Engineering And Telecommunications Hallym University

関連論文

• A Local Learning Framework Based on Multiple Local Classifiers(Pattern Recognition)

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