Title :
A novel concept for first order learning algorithm design
Author :
Geczy, Peter ; Usui, Shiro
Author_Institution :
RIKEN, Inst. of Phys. & Chem. Res., Saitama, Japan
Abstract :
One of the essential problems in the neural network field is the fact that some learning techniques perform well on certain classes of problems and fail on the others. Conventional approaches to training neural networks overlook the important link between the learning algorithm and the learning task. Ignoring such evidence leads to various controversies. To resolve the issue requires us to establish a suitable classification framework for both learning algorithms and learning tasks
Keywords :
convergence; learning (artificial intelligence); multilayer perceptrons; optimisation; classification framework; first order learning algorithm design; learning task; neural network training; Algorithm design and analysis; Biological neural networks; Convergence; Joining processes; Laboratories; Neuroscience; Optimization methods; Search methods; Stability; Stochastic processes;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.939050