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|Title:||Global Optimization versus Deterministic Pruning for the Classification of Remotely Sensed Imagery|
|Authors:||STATHAKIS Dimitrios; KANELLOPOULOS Ioannis|
|Citation:||PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING vol. 74 no. 10 p. 1259-1265|
|Publisher:||AMER SOC PHOTOGRAMMETRY|
|JRC Publication N°:||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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