Title :
Monitoring the Formation of Kernel-Based Topographic Maps in a Hybrid SOM-kMER Model
Author :
Chee Siong Teh ; Chee Peng Lim
Author_Institution :
Fac. of Cognitive Sci. & Human Dev., Univ. Malaysia Sarawak, Kota Samarahan
Abstract :
A new lattice disentangling monitoring algorithm for a hybrid self-organizing map-kernel-based maximum entropy learning rule (SOM-kMER) model is proposed. It aims to overcome topological defects owing to a rapid decrease of the neighborhood range over the finite running time in topographic map formation. The empirical results demonstrate that the proposed approach is able to accelerate the formation of a topographic map and, at the same time, to simplify the monitoring procedure
Keywords :
learning (artificial intelligence); maximum entropy methods; self-organising feature maps; hybrid SOM-kMER model; kernel-based maximum entropy learning rule; kernel-based topographic maps; lattice disentangling monitoring algorithm; self-organizing map; Acceleration; Data mining; Data visualization; Entropy; Lattices; Monitoring; Network topology; Neural networks; Prototypes; Surface topography; Kernel-based maximum entropy learning rule (kMER); kernel-based topographic map; lattice disentangling; self-organizing map (SOM); Algorithms; Artificial Intelligence; Cluster Analysis; Computing Methodologies; Information Storage and Retrieval; Neural Networks (Computer); Pattern Recognition, Automated;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2006.877536
