Kalman filter implementation of self-organizing feature maps
Neural Computation
SOAN: Self Organizing with Adaptive Neighborhood Neural Network
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
TASOM: The Time Adaptive Self-Organizing Map
ITCC '00 Proceedings of the The International Conference on Information Technology: Coding and Computing (ITCC'00)
Active audition using the parameter-less self-organising map
Autonomous Robots
TASOM: a new time adaptive self-organizing map
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multiple contour extraction from graylevel images using an artificial neural network
IEEE Transactions on Image Processing
Fast self-organizing feature map algorithm
IEEE Transactions on Neural Networks
A noisy self-organizing neural network with bifurcation dynamics for combinatorial optimization
IEEE Transactions on Neural Networks
Self-organizing learning array
IEEE Transactions on Neural Networks
The parameterless self-organizing map algorithm
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
The original Parameter-Less Self-Organising Map (PLSOM) algorithm was introduced as a solution to the problems the Self-Organising Map (SOM) encounters when dealing with certain types of mapping tasks. Unfortunately the PLSOM suffers from over-sensitivity to outliers and over-reliance on the initial weight distribution. The PLSOM2 algorithm is introduced to address these problems with the PLSOM. PLSOM2 is able to cope well with outliers without exhibiting the problems associated with the standard PLSOM algorithm. The PLSOM2 requires very little computational overhead compared to the standard PLSOM, thanks to an efficient method of approximating the diameter of the inputs, and does not rely on a priori knowledge of the training input space. This paper provides a discussion of the reasoning behind the PLSOM2 and experimental results showing its effectiveness for mapping tasks.