A Pattern Classifier--Modified AFC, and Handwritten Digit Recognition

Yitong ZHANG; Hideya TAKAHASHI; Kazuo SHIGETA; Eiji SHIMIZU

A Pattern Classifier--Modified AFC, and Handwritten Digit Recognition

Yitong ZHANG, Hideya TAKAHASHI, Kazuo SHIGETA, Eiji SHIMIZU

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We modified the adaptive fuzzy classification algorithm (AFC), which allows fuzzy clusters to grow to meet the demands of a given task during training. Every fuzzy cluster is defined by a reference vector and a fuzzy cluster radius, and it is represented as a shape of hypersphere in pattern space. Any pattern class is identified by overlapping plural hyperspherical fuzzy clusters so that it is possible to approximate complex decision boundaries among pattern classes. The modified AFC was applied to recognize handwritten digits, and performances were shown compared with other neural networks.

Publication: IEICE TRANSACTIONS on Information Vol.E77-D No.10 pp.1179-1185

Publication Date: 1994/10/25

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Type of Manuscript: PAPER

Category: Artificial Intelligence and Cognitive Science

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Yitong ZHANG, Hideya TAKAHASHI, Kazuo SHIGETA, Eiji SHIMIZU, "A Pattern Classifier--Modified AFC, and Handwritten Digit Recognition" in IEICE TRANSACTIONS on Information, vol. E77-D, no. 10, pp. 1179-1185, October 1994, doi: .
Abstract: We modified the adaptive fuzzy classification algorithm (AFC), which allows fuzzy clusters to grow to meet the demands of a given task during training. Every fuzzy cluster is defined by a reference vector and a fuzzy cluster radius, and it is represented as a shape of hypersphere in pattern space. Any pattern class is identified by overlapping plural hyperspherical fuzzy clusters so that it is possible to approximate complex decision boundaries among pattern classes. The modified AFC was applied to recognize handwritten digits, and performances were shown compared with other neural networks.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e77-d_10_1179/_p

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@ARTICLE{e77-d_10_1179,
author={Yitong ZHANG, Hideya TAKAHASHI, Kazuo SHIGETA, Eiji SHIMIZU, },
journal={IEICE TRANSACTIONS on Information},
title={A Pattern Classifier--Modified AFC, and Handwritten Digit Recognition},
year={1994},
volume={E77-D},
number={10},
pages={1179-1185},
abstract={We modified the adaptive fuzzy classification algorithm (AFC), which allows fuzzy clusters to grow to meet the demands of a given task during training. Every fuzzy cluster is defined by a reference vector and a fuzzy cluster radius, and it is represented as a shape of hypersphere in pattern space. Any pattern class is identified by overlapping plural hyperspherical fuzzy clusters so that it is possible to approximate complex decision boundaries among pattern classes. The modified AFC was applied to recognize handwritten digits, and performances were shown compared with other neural networks.},
keywords={},
doi={},
ISSN={},
month={October},}

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TY - JOUR
TI - A Pattern Classifier--Modified AFC, and Handwritten Digit Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 1179
EP - 1185
AU - Yitong ZHANG
AU - Hideya TAKAHASHI
AU - Kazuo SHIGETA
AU - Eiji SHIMIZU
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E77-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 1994
AB - We modified the adaptive fuzzy classification algorithm (AFC), which allows fuzzy clusters to grow to meet the demands of a given task during training. Every fuzzy cluster is defined by a reference vector and a fuzzy cluster radius, and it is represented as a shape of hypersphere in pattern space. Any pattern class is identified by overlapping plural hyperspherical fuzzy clusters so that it is possible to approximate complex decision boundaries among pattern classes. The modified AFC was applied to recognize handwritten digits, and performances were shown compared with other neural networks.
ER -