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An Empirical Study of a Simple Incremental Classifier Based on Vector Quantization and Adaptive Resonance Theory Cover

An Empirical Study of a Simple Incremental Classifier Based on Vector Quantization and Adaptive Resonance Theory

International Journal of Applied Mathematics and Computer Science
Volume 34 (2024): Issue 1 (March 2024)
By: Sylwester Czmil,  Jacek Kluska and  Anna Czmil  
Open Access
|Mar 2024

Authors

Sylwester Czmil

sylwesterczmil@gmail.com

Department of Control and Computer Engineering, Rzeszow University of Technology, Rzeszów, Poland

Jacek Kluska

Department of Control and Computer Engineering, Rzeszow University of Technology, Rzeszów, Poland

Anna Czmil

Department of Control and Computer Engineering, Rzeszow University of Technology, Rzeszów, Poland
DOI: https://doi.org/10.61822/amcs-2024-0011 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
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Language: English
Page range: 149 - 165
Submitted on: Jun 1, 2023
Accepted on: Sep 24, 2023
Published on: Mar 26, 2024
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
incremental learning,
data classification,
vector quantization,
adaptive resonance theory,
classification performance
Related subjects:
Mathematics,
Applied mathematics

© 2024 Sylwester Czmil, Jacek Kluska, Anna Czmil, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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