Ensemble of structure-adaptive self-organizing maps for high performance classification

Sung Bae Cho

doi:10.1016/S0020-0255(99)00112-7

Ensemble of structure-adaptive self-organizing maps for high performance classification

Sung Bae Cho

College of Computing

Research output: Contribution to journal › Article › peer-review

19 Citations (Scopus)

Abstract

Combining multiple models has been recently exploited for the development of reliable neural networks. This paper introduces a structure-adaptive self-organizing map (SOM) which can adapt the structure as well as the weights, and presents a method to improve the performance by combining the multiple maps. The structure-adaptive SOM places the nodes of prototype vectors into the pattern space properly so as to make the decision boundaries as close to the class boundaries as possible. In order to show the performance of the proposed method, experiments with the unconstrained handwritten digit database of Concordia University in Canada have been conducted.

Original language	English
Pages (from-to)	103-114
Number of pages	12
Journal	Information sciences
Volume	123
Issue number	1
DOIs	https://doi.org/10.1016/S0020-0255(99)00112-7
Publication status	Published - 2000 Mar

Bibliographical note

Funding Information:
This work was supported in part by a grant from the Brain Science Research Center with the brain research program of the Ministry of Science and Technology in Korea.

All Science Journal Classification (ASJC) codes

Software
Control and Systems Engineering
Theoretical Computer Science
Computer Science Applications
Information Systems and Management
Artificial Intelligence

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10.1016/S0020-0255(99)00112-7

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abstract = "Combining multiple models has been recently exploited for the development of reliable neural networks. This paper introduces a structure-adaptive self-organizing map (SOM) which can adapt the structure as well as the weights, and presents a method to improve the performance by combining the multiple maps. The structure-adaptive SOM places the nodes of prototype vectors into the pattern space properly so as to make the decision boundaries as close to the class boundaries as possible. In order to show the performance of the proposed method, experiments with the unconstrained handwritten digit database of Concordia University in Canada have been conducted.",

author = "Cho, {Sung Bae}",

note = "Funding Information: This work was supported in part by a grant from the Brain Science Research Center with the brain research program of the Ministry of Science and Technology in Korea. ",

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AB - Combining multiple models has been recently exploited for the development of reliable neural networks. This paper introduces a structure-adaptive self-organizing map (SOM) which can adapt the structure as well as the weights, and presents a method to improve the performance by combining the multiple maps. The structure-adaptive SOM places the nodes of prototype vectors into the pattern space properly so as to make the decision boundaries as close to the class boundaries as possible. In order to show the performance of the proposed method, experiments with the unconstrained handwritten digit database of Concordia University in Canada have been conducted.

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Ensemble of structure-adaptive self-organizing maps for high performance classification

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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