Self-Organizing Maps
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Binary tree time adaptive self-organizing map
Neurocomputing
A recurrent multimodal network for binding written words and sensory-based semantics into concepts
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
TASOM: a new time adaptive self-organizing map
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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We present a modification of the well-known Self-Organizing Map (SOM) in which we incrementally allocate the neuronal nodes to progressively added new stimuli. Our incremental SOM (iSOM) aims at the situation when a stimulus, or percept, is represented by a number of neuronal nodes a typical case in biological situation when the redundancy of representation of data is important. The iSOM is applied to categorization of visual objects using the recently introduced feature vector based on the angular integral of the Radon transform [10].