HiddenResilientGradientLearningT Properties 
The HiddenResilientGradientLearningT generic type exposes the following members.
Name  Description  

CurrentIteration 
Gets or sets the number of performed iterations.
 
DecreaseFactor 
Gets the decrease parameter, also
referred as eta minus. Default is 0.5.
 
Function 
Gets or sets the potential function to be used if this learning algorithm
needs to create a new HiddenConditionalRandomFieldT.
(Inherited from BaseHiddenConditionalRandomFieldLearningT.)  
HasConverged 
Gets or sets whether the algorithm has converged.
 
IncreaseFactor 
Gets the increase parameter, also
referred as eta plus. Default is 1.2.
 
Iterations  Obsolete.
Please use MaxIterations instead.
 
MaxIterations 
Gets or sets the maximum number of iterations
performed by the learning algorithm.
 
Model 
Gets or sets the model being trained.
(Inherited from BaseHiddenConditionalRandomFieldLearningT.)  
ParallelOptions 
Gets or sets the parallelization options for this algorithm.
 
Regularization 
Gets or sets the amount of the parameter weights
which should be included in the objective function.
Default is 0 (do not include regularization).
 
Stochastic 
Gets or sets a value indicating whether this HiddenGradientDescentLearningT
should use stochastic gradient updates. Default is true.
 
Token 
Gets or sets a cancellation token that can be used to
stop the learning algorithm while it is running.
(Inherited from BaseHiddenConditionalRandomFieldLearningT.)  
Tolerance 
Gets or sets the maximum change in the average loglikelihood
after an iteration of the algorithm used to detect convergence.
 
UpdateLowerBound 
Gets or sets the minimum possible update step,
also referred as delta max. Default is 1e6.
 
UpdateUpperBound 
Gets or sets the maximum possible update step,
also referred as delta min. Default is 50.
