An agent enabling personalized learning in e-learning environments
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
TASOM: The Time Adaptive Self-Organizing Map
ITCC '00 Proceedings of the The International Conference on Information Technology: Coding and Computing (ITCC'00)
Improved SOM learning using simulated annealing
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Adaptive FIR neural model for centroid learning in self-organizing maps
IEEE Transactions on Neural Networks
A sequential algorithm for training the SOM prototypes based on higher-order recursive equations
Advances in Artificial Neural Systems
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Presents an extension of the self-organizing learning algorithm of feature maps in order to improve its convergence to neighborhood preserving maps. The Kohonen learning algorithm is controlled by two learning parameters, which have to be chosen empirically because there exists neither rules nor a method for their calculation. Consequently, often time consuming parameter studies have to precede before a neighborhood preserving feature map is obtained. To circumvent those lengthy numerical studies, here, a method is presented and incorporated into the learning algorithm which determines the learning parameters automatically. Therefore, system models of the learning and organizing process are developed in order to be followed and predicted by linear and extended Kalman filters. The learning parameters are optimal within the system models, so that the self-organizing process converges automatically to a neighborhood preserving feature map of the learning data