Title :
Quantizability and learning complexity in multilayer neural networks
Author_Institution :
Dept. of Comput. & Inf. Sci., Florida Univ., Gainesville, FL, USA
fDate :
5/1/1998 12:00:00 AM
Abstract :
The relationship between quantizability and learning complexity in multilayer neural networks is examined. In a special neural network architecture that calculates node activations according to the certainty factor (CF) 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. This analysis is further supported by empirical simulation results
Keywords :
expert systems; feedforward neural nets; generalisation (artificial intelligence); learning (artificial intelligence); multilayer perceptrons; neural net architecture; quantisation (signal); simulation; certainty factor model; empirical simulation; expert systems; generalization dimensionality; learning complexity; multilayer neural networks; neural network architecture; node activations; quantizability; Analytical models; Computer networks; Degradation; Expert systems; Intelligent networks; Machine learning; Multi-layer neural network; Multilayer perceptrons; Neural networks; Quantization;
Journal_Title :
Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on
DOI :
10.1109/5326.669575
