Estimating the Data Region Using Minimum and Maximum Values

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概要

• 論文の詳細を見る

• In the field of pattern recognition or outlier detection, it is often necessary to estimate the region where data of a particular class are generated. In other words, it is required to accurately estimate the support of the distribution that generates the data. Considering the 1-dimensional distribution whose support is a finite interval, the data region is estimated effectively by the maximum value and the minimum value in the samples. Limiting distributions of these values have been studied in the extreme-value theory in statistics. In this research, we propose a method to estimate the data region using the maximum value and the minimum value in the samples. We show the average loss of the estimator and derive the optimally improved estimators for given loss functions. The method can be extended to estimate the higher dimensional input space.

• 東北大学の論文

著者

• Watanabe Sumio

  P&i Lab. Tokyo Inst. Of Technol. Jpn

• WATANABE Kazuho

  Department of Computational Intelligence and Systems Science,Tokyo Institute of Technology

• WATANABE Sumio

  Precision and Intelligence Laboratory, Tokyo Institute of Technology

• Watanabe Kazuho

  Department Of Agricultural Chemistry Faculty Of Agriculture Tohoku University:(present Address) Inst

• Watanabe Sumio

  Precision And Intelligence Laboratory Tokyo Institute Of Technology

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