A pattern reordering approach based on ambiguity detection for online category learning

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.