LevenbergMarquardtLearning Properties 
The LevenbergMarquardtLearning type exposes the following members.
Name  Description  

Adjustment 
Learning rate adjustment. Default value is 10.
 
Alpha 
Gets or sets the importance of the squared sum of network
weights in the cost function. Used by the regularization.
 
Beta 
Gets or sets the importance of the squared sum of network
errors in the cost function. Used by the regularization.
 
Blocks 
Gets or sets the number of blocks to divide the
Jacobian matrix in the Hessian calculation to
preserve memory. Default is 1.
 
EffectiveParameters 
Gets the number of effective parameters being used
by the network as determined by the Bayesian regularization.
 
Gradient 
Gets the gradient vector computed in the last iteration.
 
Hessian 
Gets the approximate Hessian matrix of second derivatives
generated in the last algorithm iteration. The Hessian is
stored in the upper triangular part of this matrix. See
remarks for details.
 
Jacobian 
Gets the Jacobian matrix created in the last iteration.
 
LearningRate 
Levenberg's damping factor (lambda). This
value must be positive. Default is 0.1.
 
NumberOfParameters 
Gets the total number of parameters
in the network being trained.
 
ParallelOptions 
Gets or sets the parallelization options for this algorithm.
 
UseRegularization 
Gets or sets whether to use Bayesian Regularization.
