Title :
A learning law for density estimation
Author :
Modha, Dharmendra S. ; Fainman, Yeshayahu
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA
fDate :
5/1/1994 12:00:00 AM
Abstract :
Probability density functions are estimated by an exponential family of densities based on multilayer feedforward networks. The role of the multilayer feedforward networks, in the proposed estimator, is to approximate the logarithm of the probability density functions. The method of maximum likelihood is used, as the main contribution, to derive an unsupervised backpropagation learning law to estimate the probability density functions. Computer simulation results demonstrating the use of the derived learning law are presented
Keywords :
backpropagation; feedforward neural nets; probability; exponential family; maximum likelihood; multilayer feedforward networks; probability density function estimation; unsupervised backpropagation learning law; Feedforward neural networks; Function approximation; Linear approximation; Multi-layer neural network; Multilayer perceptrons; Neural networks; Neurons; Optimized production technology; Upper bound;
Journal_Title :
Neural Networks, IEEE Transactions on
