A Copy-Learning Model for Recognizing Patterns Rotated at Various Angles

Kenichi SUZAKI; Shinji ARAYA; Ryozo NAKAMURA

A Copy-Learning Model for Recognizing Patterns Rotated at Various Angles

Kenichi SUZAKI, Shinji ARAYA, Ryozo NAKAMURA

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In this paper we discuss a neural network model that can recognize patterns rotated at various angles. The model employs copy learning, a learning method entirely different from those used in conventional models. Copy-Learning is an effective learning method to attain the desired objective in a short period of time by making a copy of the result of basic learning through the application of certain rules. Our model using this method is capable of recognizing patterns rotated at various angles without requiring mathematical preprocessing. It involves two processes: first, it learns only the standard patterns by using part of the network. Then, it copies the result of the learning to the unused part of the network and thereby recognizes unknown input patterns by using all parts of the network. The model has merits over the conventional models in that it substantially reduces the time required for learning and recognition and can also recognize the rotation angle of the input pattern.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E76-A No.7 pp.1207-1211

Publication Date: 1993/07/25

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Type of Manuscript: Special Section LETTER (Special Section of Letters Selected from the 1993 IEICE Spring Conference)

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Kenichi SUZAKI, Shinji ARAYA, Ryozo NAKAMURA, "A Copy-Learning Model for Recognizing Patterns Rotated at Various Angles" in IEICE TRANSACTIONS on Fundamentals, vol. E76-A, no. 7, pp. 1207-1211, July 1993, doi: .
Abstract: In this paper we discuss a neural network model that can recognize patterns rotated at various angles. The model employs copy learning, a learning method entirely different from those used in conventional models. Copy-Learning is an effective learning method to attain the desired objective in a short period of time by making a copy of the result of basic learning through the application of certain rules. Our model using this method is capable of recognizing patterns rotated at various angles without requiring mathematical preprocessing. It involves two processes: first, it learns only the standard patterns by using part of the network. Then, it copies the result of the learning to the unused part of the network and thereby recognizes unknown input patterns by using all parts of the network. The model has merits over the conventional models in that it substantially reduces the time required for learning and recognition and can also recognize the rotation angle of the input pattern.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e76-a_7_1207/_p

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@ARTICLE{e76-a_7_1207,
author={Kenichi SUZAKI, Shinji ARAYA, Ryozo NAKAMURA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Copy-Learning Model for Recognizing Patterns Rotated at Various Angles},
year={1993},
volume={E76-A},
number={7},
pages={1207-1211},
abstract={In this paper we discuss a neural network model that can recognize patterns rotated at various angles. The model employs copy learning, a learning method entirely different from those used in conventional models. Copy-Learning is an effective learning method to attain the desired objective in a short period of time by making a copy of the result of basic learning through the application of certain rules. Our model using this method is capable of recognizing patterns rotated at various angles without requiring mathematical preprocessing. It involves two processes: first, it learns only the standard patterns by using part of the network. Then, it copies the result of the learning to the unused part of the network and thereby recognizes unknown input patterns by using all parts of the network. The model has merits over the conventional models in that it substantially reduces the time required for learning and recognition and can also recognize the rotation angle of the input pattern.},
keywords={},
doi={},
ISSN={},
month={July},}

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TY - JOUR
TI - A Copy-Learning Model for Recognizing Patterns Rotated at Various Angles
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1207
EP - 1211
AU - Kenichi SUZAKI
AU - Shinji ARAYA
AU - Ryozo NAKAMURA
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E76-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 1993
AB - In this paper we discuss a neural network model that can recognize patterns rotated at various angles. The model employs copy learning, a learning method entirely different from those used in conventional models. Copy-Learning is an effective learning method to attain the desired objective in a short period of time by making a copy of the result of basic learning through the application of certain rules. Our model using this method is capable of recognizing patterns rotated at various angles without requiring mathematical preprocessing. It involves two processes: first, it learns only the standard patterns by using part of the network. Then, it copies the result of the learning to the unused part of the network and thereby recognizes unknown input patterns by using all parts of the network. The model has merits over the conventional models in that it substantially reduces the time required for learning and recognition and can also recognize the rotation angle of the input pattern.
ER -