Constructs a QuasiNewtonTrainer object.

Clones a copy of the trainer.

Determines whether the specified Object is equal to the current Object.

Allows an Object to attempt to free resources and perform other cleanup operations before the Object is reclaimed by garbage collection.

Returns the function used to compute the error to be minimized.

Serves as a hash function for a particular type.

Gets the Type of the current instance.

Creates a shallow copy of the current Object.

Sets the function that computes the network error.

Returns a String that represents the current Object.

Trains the neural network using supplied training patterns.

Compute the sum of squares error.

The epoch number for the trainer.

The error function used by the trainer.

The value of the gradient of the error function with respect to the Weights.

The error status from the trainer.

The final value of the error function.

The gradient tolerance.

The maximum step size.

The maximum number of iterations to use in a training.

Perform the parallel calculations with the maximum possible number of processors set to NumberOfProcessors.

The trainer to be used in multi-threaded EpochTainer.

The scaled step tolerance.

The number of iterations used during training.

Specify the use of the back propagation algorithm for gradient calculations during network training.