DocumentCode
1749083
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
Volume
1
fYear
2001
fDate
2001
Firstpage
382
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-7044-9
Type
conf
DOI
10.1109/IJCNN.2001.939050
Filename
939050
Link To Document