Partially Supervised Learning for Nearest Neighbor Classifiers

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概要

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• A learning algorithm is presented for nearest neighbor pattern classifiers for the cases where mixed supervised and unsupervised training data are given. The classification rule includes rejection of outlier patterns and fuzzy classification. This partially supervised learning problem is formulated as a multiobjective program which reduces to purely super-vised case when all training data are supervised or to the other extreme of fully unsupervised one when all data are unsupervised. The learning, i. e. the solution process of this program is performed with a gradient method for searching a saddle point of the Lagrange function of the program.

• 社団法人電子情報通信学会の論文
• 1996-02-25

著者

• URAHAMA Kiichi

  Department of Computer Science and Communication Engineering, Kyushu University

• Matsunaga H

  Department Of Visual Communication Design Kyushu Institute Of Design

• Matsunaga Hiroyuki

  Department Of Surgery Tsushima City Hospital

• Urahama Kiichi

  Department Of Visual Communication Design Kyushu Institute Of Design

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