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- Title
Global Optimization versus Deterministic Pruning for the Classification of Remotely Sensed Imagery.
- Authors
Stathakis, D.; Kanellopoulos, I.
- Abstract
The effect of pruning neural network structures in remote sensing is investigated. Standard pruning methods, i.e., Optimal Brain Damage and Optimal Brain Surgeon, are compared with pruning based on a genetic algorithm. Direct coding is used to represent the links of the network for optimization with a canonical genetic algorithm using binary representation. The results show that the genetic algorithm is the only method able to discover a significantly better neural network structure. The main drawback of the genetic approach is the extensive training time required.
- Subjects
ARTIFICIAL neural networks; REMOTE sensing; GENETIC algorithms; ARTIFICIAL intelligence; ALGORITHMS; COMBINATORIAL optimization
- Publication
Photogrammetric Engineering & Remote Sensing, 2008, Vol 74, Issue 10, p1259
- ISSN
0099-1112
- Publication type
Article
- DOI
10.14358/PERS.74.10.1259