A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Algorithms for clustering data
Algorithms for clustering data
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Competitive learning algorithms for vector quantization
Neural Networks
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A comparison of self-organizing neural networks for fast clustering of radar pulses
Signal Processing - Special issue on neural networks
Clustering Algorithms
A Pattern Reordering Approach Based on Ambiguity Detection for Online Category Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Comparative analysis of fuzzy ART and ART-2A network clustering performance
IEEE Transactions on Neural Networks
A Pattern Reordering Approach Based on Ambiguity Detection for Online Category Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online crowdsourcing subjective image quality assessment
Proceedings of the 20th ACM international conference on Multimedia
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Pattern reordering is proposed as an alternative to sequential and batch processing for online category learning. Upon detecting that the categorization of a new input pattern is ambiguous, the input is postponed for a predefined time, after which it is reexamined and categorized for good. This approach is shown to improve the categorization performance over purely sequential processing, while yielding a shorter input response time, or latency, than batch processing. In order to examine the response time of processing schemes, the latency of a typical implementation is derived and compared to lower bounds. Gaussian and softmax models are derived from reject option theory and are considered for detecting ambiguity and triggering pattern postponement. The average latency and Rand Adjusted clustering score of reordered, sequential, and batch processing are compared through computer simulation using two unsupervised competitive learning neural networks and a radar pulse data set.