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 :
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