直観的な学習制御パラメータを有するＡｒｃｉｎｇアルゴリズム

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  • 小野田 崇
    (財)電力中央研究所情報研究所
  • Rätsch Gunnar
    Research Institute for Computer Architecture and Software Technology, German National Research Center for Information Technology.
  • Müller Klaus R.
    Research Institute for Computer Architecture and Software Technology, German National Research Center for Information Technology.

書誌事項

タイトル別名
  • An Arcing algorithm with an intuitive learning control parameter
  • チョッカンテキ ナ ガクシュウ セイギョ パラメータ オ ユウスル Arcing アルゴリズム

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抄録

AdaBoost has been successfully applied to a number of classification tasks, seemingly defying problems of overfitting. AdaBoost performs gradient descent in an error function with respect to the margin. This method concentrates on the patterns which are hardest to learn. However, this property of AdaBoost can be disadvantageous for noisy problems. Indeed, theoretical analysis has shown that the margin distribution plays a crucial role in understanding this phenomenon. Loosely speaking, some outliers should be tolerated if this has the benefit of substantially increasing the margin on the remaining points. In this paper, we propose new noise robust boosting methods using the concepts of ν-Support Vector Classification and Arc-GV. These methods allow for the probability of a pre-specified fraction ν of points to lie in the margin area or even on the wrong side of the decision boundary. This algorithms can give a nicely interpretable way of controlling the trade-off between minimizing the training error and capacity.

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