The 'Neural' Phonetic Typewriter
Computer
Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
Incremental grid growing: Encoding high-dimensional structure into a two-dimensional feature map
Incremental grid growing: Encoding high-dimensional structure into a two-dimensional feature map
Image compression by self-organized Kohonen map
IEEE Transactions on Neural Networks
Self-organizing feature maps with self-adjusting learning parameters
IEEE Transactions on Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
A TASOM-based algorithm for active contour modeling
Pattern Recognition Letters
An improved time-adaptive self-organizing map for high-speed shape modeling
Pattern Recognition
Applied Intelligence
Binary tree time adaptive self-organizing map
Neurocomputing
Pattern Recognition Letters
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The time-decreasing learning rate and neighborhood function of the basic SOM (Self-Organizing Map) algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changing environments, we propose a modified SOM algorithm called 驴Time Adaptive SOM驴, or TASOM, that automatically adjusts learning rate and neighborhood size of each neuron independently.The proposed TASOM is tested with stationary environments and its performance is compared with that of the basic SOM. It is also tested with non-stationary environments for representing the letter 驴L驴, which may be translated, rotated, or scaled. Moreover, the TASOM is used for adaptive segmentation of images, which may have undergone gray-level transformation.