Title: Global Optimization versus Deterministic Pruning for the Classification of Remotely Sensed Imagery
Authors: STATHAKIS DimitriosKANELLOPOULOS Ioannis
Citation: PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING vol. 74 no. 10 p. 1259-1265
Publisher: AMER SOC PHOTOGRAMMETRY
Publication Year: 2008
JRC Publication N°: JRC47194
ISSN: 0099-1112
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC47194
Type: Articles in Journals
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.
JRC Institute:Institute for Environment and Sustainability

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