rprop {CaDENCE} | R Documentation |
From Riedmiller (1994): Rprop stands for 'Resilient backpropagation' and is a local adaptive learning scheme. The basic principle of Rprop is to eliminate the harmful influence of the size of the partial derivative on the weight step. As a consequence, only the sign of the derivative is considered to indicate the direction of the weight update. The size of the weight change is exclusively determined by a weight-specific, so called 'update-value'.
This function implements the iRprop+ algorithm from Igel and Huesken (2003).
rprop(w, f, iterlim = 100, print.level = 1, delta.0 = 0.1,
delta.min = 1e-06, delta.max = 50, epsilon = 1e-08,
step.tol = 1e-06, f.target = -Inf, ...)
w |
the starting parameters for the minimization. |
f |
the function to be minimized. If the function value has an attribute called |
iterlim |
the maximum number of iterations before the optimization is stopped. |
print.level |
the level of printing which is done during optimization. A value of |
delta.0 |
size of the initial Rprop update-value. |
delta.min |
minimum value for the adaptive Rprop update-value. |
delta.max |
maximum value for the adaptive Rprop update-value. |
epsilon |
step-size used in the finite difference calculation of the gradient. |
step.tol |
convergence criterion. Optimization will stop if the change in |
f.target |
target value of |
... |
further arguments to be passed to |
A list with elements:
par |
The best set of parameters found. |
value |
The value of |
gradient |
An estimate of the gradient at the solution found. |
Igel, C. and M. Huesken, 2003. Empirical evaluation of the improved Rprop learning algorithms. Neurocomputing 50: 105-123.
Riedmiller, M., 1994. Advanced supervised learning in multilayer perceptrons - from backpropagation to adaptive learning techniques. Computer Standards and Interfaces 16(3): 265-278.