Improving the Rprop Learning Algorithm (2000)

by Christian Igel , Michael Hüsken
Venue:PROCEEDINGS OF THE SECOND INTERNATIONAL SYMPOSIUM ON NEURAL COMPUTATION (NC 2000)
Citations:41 - 7 self

Active Bibliography

55 Empirical evaluation of the improved Rprop learning algorithms – Christian Igel, Michael Hüsken - 2003
unknown title – Psfrag Replacements - 1997
6 Discovering Efficient Learning Rules for Feedforward Neural Networks using Genetic Programming – Amr Radi, Riccardo Poli - 2002
58 Local Gain Adaptation in Stochastic Gradient Descent – Nicol N. Schraudolph - 1999
3 Fast Learning for Problem Classes Using Knowledge Based Network Initialization – Michael Hüsken, Christian Goerick - 2000
1 AN ARTIFICIAL NEURAL NETWORK METHOD FOR SOLVING BOUNDARY VALUE PROBLEMS WITH ARBITRARY IRREGULAR BOUNDARIES Approved by: – Kevin S. Mcfall, Dr. J. Robert Mahan, Dr. Nader Sadegh - 2006
5 Classification-Based Objective Functions – Michael Rimer, Tony Martinez - 2007
7 Gradient Descent: Second-Order Momentum and Saturating Error – Barak Pearlmutter - 1992
12 Efficient Training of Feed-Forward Neural Networks – Martin Møller - 1997
Integrated Learning in Multi-net Systems – Matthew Charles Casey - 2004
5 Rprop Using the Natural Gradient – Christian Igel, Marc Toussaint, Wan Weishui - 2005
11 Fast online policy gradient learning with smd gain vector adaptation – Nicol N. Schraudolph, Jin Yu, Douglas Aberdeen - 2006
2 Improving the Convergence of the Backpropagation Algorithm Using Local Adaptive Techniques – Z. Zainuddin, N. Mahat, Y. Abu Hassan - 2005
9 Online Independent Component Analysis with Local Learning Rate Adaptation – Nicol N. Schraudolph, Xavier Giannakopoulos - 2000
3 Online learning with adaptive local step sizes – Nicol N. Schraudolph - 1999
7 JETNET 3.0 - A Versatile Artificial Neural Network Package – Carsten Peterson, Thorsteinn Rögnvaldsson, Leif Lönnblad - 1993
Parameter Optimization Algorithm with Improved Convergence Properties for Adaptive Learning – G. D. Magoulas, M. N. Vrahatis
Effective Neural Network Training With A Different Learning Rate For Each Weight – G. D. Magoulas, V.P. Plagianakos, M.N. Vrahatis, U. P. Arti Cial Intelligence
Nonmonotone Methods for Backpropagation Training with Adaptive Learning Rate – V.P. Plagianako, M.N. Vrahatis - 1999