Name
Abstract | ||
|---|---|---|
In order to reduce the relativity among prototype pattern vectors and to enhance the separability among different patterns, a novel kernel-based learning algorithm of Synergetic Neural Network (SNN) is proposed. The algorithm first maps the data from original space into a new feature space and then classifies them by a two-layered SNN. An optimization method of weighted factors in the two-layered SNN is also presented. It gives different patterns to different weights and makes full use of the global and local searching ability of Immunity Clonal Algorithm (ICA). Experiments on Iris dataset, textural images and Synthetic Aperture Radar (SAR) images show that the new algorithm does not only improve the classification rate but also has shorter training and testing time. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1543834.1543889 | GEC Summit |
Keywords | Field | DocType |
image classification,feature space,neural network,synthetic aperture radar,local search | Kernel (linear algebra),Feature vector,Pattern recognition,Computer science,Synthetic aperture radar,Prototype pattern,Artificial intelligence,Iris flower data set,Contextual image classification,Artificial neural network,Classification rate,Machine learning | Conference |
Volume | Issue | Citations |
null | null | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiuli Ma | 1 | 92 | 15.47 |
| Guoqiang Mu | 2 | 0 | 0.34 |
| Xiaoqing Yu | 3 | 75 | 11.53 |