A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Neural Networks
An optoelectronic learning machine
An optoelectronic learning machine
On the quality of ART1 text clustering
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Distributed Sensor Networks (Chapman & Hall/Crc Computer and Information Science)
Distributed Sensor Networks (Chapman & Hall/Crc Computer and Information Science)
GFAM: Evolving Fuzzy ARTMAP neural networks
Neural Networks
Clustering
Using default ARTMAP for cancer classification with microRNA expression signatures
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
A supervised fuzzy adaptive resonance theory with distributed weight update
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
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This paper briefly summarizes some valuable properties of ART architectures that are advantageous in engineering applications, and outlines some areas of likely future progress, together with their motivations. Some of ART's advantages, such as its stability, biological plausibility, and responsiveness to the stability-plasticity dilemma, are well-described in the literature. This paper's focus will be on the advantages of scalability, speed, configurability, potential for parallelization, and ability to interpret the results. A valuable new area of innovation will be the application of ART to more generalized data structures such as trees and grammars. Continued progress on distributed representations would be valuable because of increased data representation capability, both in terms of system capacity and template complexity. Another valuable area of progress would be removal of the dichotomy between match-based and error-based learning.