Title :
Learning in a quantizable neural network
Author_Institution :
Dept. of Comput. & Inf. Sci., Florida Univ., Gainesville, FL, USA
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;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.831137
