DocumentCode
2260564
Title
Analyzing learning dynamics: How to average?
Author
Goerick, Christian
Author_Institution
Inst. fur Neuroinf., Ruhr-Univ., Bochum, Germany
Volume
2
fYear
2000
fDate
2000
Firstpage
191
Abstract
Pattern-based learning processes are usually analyzed by means of probability density functions of the weights or moments thereof. During the derivation of these equations, some averaging has to be performed. We show that the manner of averaging is crucial for the results of the analysis. We do this by comparing two types of analysis (Langevin type and discrete-time moments) for one learning system
Keywords
Markov processes; learning (artificial intelligence); neural nets; probability; Langevin type analysis; averaging; discrete-time moments analysis; learning dynamics; pattern-based learning processes; probability density functions; Difference equations; Differential equations; Evolution (biology); Information analysis; Markov processes; Nonlinear dynamical systems; Nonlinear equations; Pattern analysis; Probability density function; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.857896
Filename
857896
Link To Document