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Decision Boundary

The training process makes the network to define its decision boundaries.
Depend on Network Topology, training method and training parameters (such as learning rate and epoche) such boundaries may be formed in inclusive or exclusive paradigm.

Following Illustration shows a tight inclusive decision boundary on a RBF network with 0.01 spread constant.


Using a higher spread constant will enlarge the boundary..



Following figures show decision boundary on a MLP network with linear transfer function at output layer.




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Last Update: 09/18/2003