Introduction to the theory of neural computation
Introduction to the theory of neural computation
Self-organizing maps
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Generalized regression neural network in modelling river sediment yield
Advances in Engineering Software
A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
The paper presents a new automated pattern classification method. At first original data points are partitioned by unsupervised self-organizing map network (SOM). Then from the above clustering results, some labelled points nearer to each clustering center are chosen to train supervised generalization regression neural network model (GRNN). Then utilizing the decided GRNN model, we reclassify these original data points and gain new clustering results. At last from new clustering results, we choose some labelled points nearer to new clustering center to train and classify again, and so repeat until clustering center no longer changes. Experimental results for Iris data, Wine data and remote sensing data verify the validity of our method.