A Clustering Method Using Hierarchical Self-Organizing Maps
Journal of VLSI Signal Processing Systems
Description of Dynamic Structured Scenes by a SOM/ARSOM Hierarchy
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
A SOM/ARSOM Hierarchy for the Description of Dynamic Scenes
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Modular neural architectures for robotics
Biologically inspired robot behavior engineering
An analysis of diversity measures
Machine Learning
Topology preserving SOM with transductive confidence machine
DS'10 Proceedings of the 13th international conference on Discovery science
A handwritten Bangla numeral recognition scheme based on expanded two-layer SOM
International Journal of Intelligent Systems Technologies and Applications
Clustering high-dimensional data using growing SOM
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Patient's motion recognition based on SOM-decision tree
WASA'13 Proceedings of the 8th international conference on Wireless Algorithms, Systems, and Applications
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We develop a multilayer overlapped self-organizing maps (SOM's) with limited structure adaptation capabilities, and associated learning scheme for labeled pattern classification applications. The learning algorithm consists of the standard unsupervised SOM learning of synaptic weights as well as the supervised vector quantization learning. As higher layer SOMs overlap, the final classification is made by fusing the classifications of top-level overlapped SOMs. We obtained the best results ever reported for any SOM-based numerals classification system