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Libsvm [39] is used for training and classification.
It enjoys as well faster training and classification.
The developed ANN model used input/output experimental data for training and classification.
Incorporation of adjacency, both in training and classification, enhances the overall architecture with robustness and adaptability.
Our system uses the IOB model to annotate data in the training and classification phases.
Furthermore, both training and classification times are approximately eight times faster on all folds.
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In the training and classifications, areas under permanent and shifting cultivation were treated as separate classes.
Our experiments show very good results and confirm that the balanced training algorithm and parallel solutions are very essential for large-scale visual classification in terms of training time and classification accuracy.
Figure 7 Projection of training data and classification boundaries.
The performances of the MLPNN classifiers were evaluated in terms of training performance and classification accuracies.
The superiority of the GA-ANN method was manifested in training accuracy and classification success rate.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com