A Method of an Effective Learning for BP Network from the Data Which Contain Usual or Unusual Data

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概要

• 論文の詳細を見る

• Advances in knowledge information processing technology have brought great promise to research of pathological diseases. Using oligosaccharides as intercellular information transmitters suited to discover the causes of onset of a disease, our group has employed neural networks in developing a support system for pathological diagnosis of liver disease, and obtained good results. When considering clinical application of such systems, however, the problem of the great amount of time needed for training, due to usual and unusual data that physiological data such as that obtained through oligosaccharides includes, remains when using conventional training methods to distinguish between liver diseases. Still, there is hope for further improvements in this method. To learn this type of data efficiently, in the present study we propose selected learning and expanded selected learning, and conducted a neural network pathological discrimination of liver disease. Finally, we show that our proposed methods are effective to train usual and unusual data.

• バイオメディカル・ファジィ・システム学会の論文

著者

• IWATA Akira

  Department of Computer Science & Engineering, Nagoya Institute of Technology

• Shirai Tatsuya

  Department Of Electrical & Computer Engineering Nagoya Institute Of Technology

• OGURI Koji

  Faculty of Information Science and Technology, Aichi Prefectural University

• Oguri Koji

  Faculty Of Information Science And Technology Aichi Prefectural University

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