Abstract
Artificial neural network algorithms were originally designed to model human neural activities. They attempt to recreate the processes involved in such activities as learning, short term memory, and long-term memory. Two widely used unsupervised artificial neural network algorithms are the Self-Organizing Map (SOM) and Adaptive Resonance Theory (ART2). Each was designed to simulate a particular biological neural activity. Both can be used as unsupervised data classifiers. This paper compares performance characteristics of two unsupervised artificial neural network architectures; the SOM and the ART2 networks. The primary factors analyzed were classification accuracy, sensitivity to data noise, and sensitivity of the algorithm control parameters. Guidelines are developed for algorithm selection.
Recommended Citation
J. J. Aleshunas et al., "Classification Characteristics of SOM and ART2," Proceedings of the ACM Symposium on Applied Computing, pp. 297 - 302, Association for Computing Machinery, Apr 1994.
The definitive version is available at https://doi.org/10.1145/326619.326752
Department(s)
Mathematics and Statistics
Second Department
Computer Science
Keywords and Phrases
ART2; Neural network; SOM; Unsupervised learning
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Association for Computing Machinery, All rights reserved.
Publication Date
06 Apr 1994
