Encog.Neural.Networks.Training.Strategy

Class	Description
Greedy	A simple greedy strategy. If the last iteration did not improve training, then discard it. Care must be taken with this strategy, as sometimes a training algorithm may need to temporarily decrease the error level before improving it.
HybridStrategy	A hybrid stragey allows a secondary training algorithm to be used. Once the primary algorithm is no longer improving by much, the secondary will be used. Using simulated annealing in as a secondary to one of the propagation methods is often a very efficient combination as it can help the propagation method escape a local minimum. This is particularly true with backpropagation.
ResetStrategy	The reset strategy will reset the weights if the neural network fails to fall below a specified error by a specified number of cycles. This can be useful to throw out initially "bad/hard" random initializations of the weight matrix.
SmartLearningRate	Attempt to automatically set the learning rate in a learning method that supports a learning rate.
SmartMomentum	Attempt to automatically set a momentum in a training algorithm that supports momentum.
StopTrainingStrategy	This strategy will indicate once training is no longer improving the neural network by a specified amount, over a specified number of cycles. This allows the program to automatically determine when to stop training.

Encog.Neural.Networks.Training.Strategy Namespace