| TR#: | CIS9420 |
| Class: | CIS |
| Title: | DENSITY SHAPING BY NEURAL NETWORKS
WITH APPLICATION TO CLASSIFICATION, ESTIMATION AND FORECASTING |
| Authors: | Y. Baram and Z. Roth |
| CIS9420.pdf | |
| Abstract: |
An estimate of the probability density function of a random vector is
obtained by maximizing the output entropy of a feed-forward network of
sigmoidal units with respect to the input weights. Classification
problems can be solved by selecting the class associated with the maximal
estimated density. Newton's method, applied to the estimated density, yields a
recursive estimator for a random variable or a random sequence. A constrained
connectivity structure yields a linear estimator, which is particularly suitable
for `real time' prediction. Applications to real classification and forecasting
problems are demonstrated. The convergence of the training algorithm is analyzed
in the appendix.
|
| Copyright | The above paper is copyright by the Technion, Author(s), or others. Please contact the author(s) for more information |
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