Partially Supervised Learning for Nearest Neighbor Classifiers

Hiroyuki MATSUNAGA; Kiichi URAHAMA

Partially Supervised Learning for Nearest Neighbor Classifiers

Hiroyuki MATSUNAGA, Kiichi URAHAMA

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E79-D No.2 pp.130-135

Publication Date: 1996/02/25

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Type of Manuscript: PAPER

Category: Image Processing,Computer Graphics and Pattern Recognition

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Hiroyuki MATSUNAGA, Kiichi URAHAMA, "Partially Supervised Learning for Nearest Neighbor Classifiers" in IEICE TRANSACTIONS on Information, vol. E79-D, no. 2, pp. 130-135, February 1996, doi: .
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/e79-d_2_130/_p

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@ARTICLE{e79-d_2_130,
author={Hiroyuki MATSUNAGA, Kiichi URAHAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Partially Supervised Learning for Nearest Neighbor Classifiers},
year={1996},
volume={E79-D},
number={2},
pages={130-135},
abstract={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.},
keywords={},
doi={},
ISSN={},
month={February},}

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TY - JOUR
TI - Partially Supervised Learning for Nearest Neighbor Classifiers
T2 - IEICE TRANSACTIONS on Information
SP - 130
EP - 135
AU - Hiroyuki MATSUNAGA
AU - Kiichi URAHAMA
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E79-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 1996
AB - 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.
ER -