DocumentCode
1809841
Title
Learning in a quantizable neural network
Author
Fu, LiMin
Author_Institution
Dept. of Comput. & Inf. Sci., Florida Univ., Gainesville, FL, USA
Volume
2
fYear
1999
fDate
36342
Firstpage
1234
Abstract
The relationship between quantizability and learning complexity in multilayer neural networks is examined. In a special neural network architecture which calculates the node activation according to the certainty factor model of expert systems, the analysis based upon quantizability leads to lower and also better estimates for generalization dimensionality and sample complexity than those suggested by the multilayer perceptron model
Keywords
feedforward neural nets; generalisation (artificial intelligence); learning (artificial intelligence); sensitivity analysis; certainty factor model; dimensionality; expert systems; generalization; learning; multilayer neural networks; node activation; quantizability; sample complexity; sensitivity analysis; Computer architecture; Computer networks; Degradation; Expert systems; Intelligent networks; Multi-layer neural network; Multilayer perceptrons; Neural networks; Quantization; Sensitivity analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-5529-6
Type
conf
DOI
10.1109/IJCNN.1999.831137
Filename
831137
Link To Document