A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Fast learning in networks of locally-tuned processing units
Neural Computation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
ART-EMAP: A neural network architecture for object recognition by evidence accumulation
IEEE Transactions on Neural Networks
Sensor Fusion in Integrated Circuit Fault Diagnosis Using a Belief Function Model
International Journal of Distributed Sensor Networks
ART-Based Neural Networks for Multi-label Classification
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Large-scale neural systems for vision and cognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Multi-label classification and extracting predicted class hierarchies
Pattern Recognition
Spectral clustering as an automated SOM segmentation tool
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Self-organizing ARTMAP rule discovery
Neural Networks
Vector quantization based approximate spectral clustering of large datasets
Pattern Recognition
Color-texture image segmentation and recognition through a biologically-inspired architecture
Pattern Recognition and Image Analysis
Hi-index | 0.01 |
Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods described here address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the automated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to the image domain.