Least-mean-square training of cluster-weighted modeling

Authors:
I-Chun Lin;Cheng-Yuan Liou
Affiliations:
Department of Computer Science and Information Engineering, National Taiwan University, Republic of China;Department of Computer Science and Information Engineering, National Taiwan University, Republic of China
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

Citing 4
Cited 3

Hierarchical mixtures of experts and the EM algorithm

Neural Computation
Self-organization using Potts models

Neural Networks
Independent component analysis using Potts models

IEEE Transactions on Neural Networks
Function approximation using generalized adalines

IEEE Transactions on Neural Networks

Three-phase strategy for the OSD learning method in RBF neural networks

Neurocomputing
An improvement in RBF learning algorithm based on PSO for real time applications

Neurocomputing
Improvement of RBF neural networks using Fuzzy-OSD algorithm in an online radar pulse classification system

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Aside from the Expectation-Maximization (EM) algorithm, Least-Mean-Square (LMS) is devised to further train the model parameters as a complementary training algorithm for Cluster-Weighted Modeling (CWM). Due to different objective functions of EM and LMS, the training result of LMS can be used to reinitialize CWM's model parameters which provides an approach to mitigate local minimum problems.