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New Kernel-Based Normality Recovery Method and Applications

새로운 커널 기반 정상 상태 복구 기법과 응용

  • 강대성 (고려대학교 제어계측공학과) ;
  • 박주영 (고려대학교 제어계측공학과)
  • Published : 2006.08.01

Abstract

The SVDD(support vector data description) is one of the most important one-class support vector learning methods, which depends on the strategy of utilizing the balls defined on the feature space to discriminate the normal data from all other possible abnormal objects. This paper addresses on the extension of the SVDD method toward the problem of recovering the normal contents from the data contaminated with noises. The validity of the proposed de-noising method is shown via application to recovering the high-resolution images from the low-resolution images based on the high-resolution training data.

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