Computer Vision
ART-EMAP: A neural network architecture for object recognition by evidence accumulation
IEEE Transactions on Neural Networks
CONFIGR: A vision-based model for long-range figure completion
Neural Networks
An Approach to Collaboration of Growing Self-Organizing Maps and Adaptive Resonance Theory Maps
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Using default ARTMAP for cancer classification with microRNA expression signatures
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Parallel ant colony optimizers with local and global ants
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Large-scale neural systems for vision and cognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Parallel ant colony optimizer based on adaptive resonance theory maps
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Self-organizing ARTMAP rule discovery
Neural Networks
On the design of a multimodal cognitive architecture for perceptual learning in industrial robots
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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The Sensor Exploitation Group of MIT Lincoln Laboratory incorporated an early version of the ARTMAP neural network as the recognition engine of a hierarchical system for fusion and data mining of registered geospatial images. The Lincoln Lab system has been successfully fielded, but is limited to target/non-target identifications and does not produce whole maps. Procedures defined here extend these capabilities by means of a mapping method that learns to identify and distribute arbitrarily many target classes. This new spatial data mining system is designed particularly to cope with the highly skewed class distributions of typical mapping problems. Specification of canonical algorithms and a benchmark testhed has enabled the evaluation of candidate recognition networks as well as pre- and post-processing and feature selection options. The resulting mapping methodology sets a standard for a variety of spatial data mining tasks. In particular, training pixels are drawn from a region that is spatially distinct from the mapped region, which could feature an output class mix that is substantially different from that of the training set. The system recognition component, default ARTMAP, with its fully specified set of canonical parameter values, has become the a priori system of choice among this family of neural networks for a wide variety of applications.