Maximum Partial Likelihood Estimation with Perceptrons
Title : Maximum Partial Likelihood Estimation with Perceptrons
Authors :
Conference : The 1993 Conference on Information Sciences and Systems pp. 812-817
Date: March 01 - March 01, 1993
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Authors :
Baras, John S.
Sonmez, Kemal M
Sonmez, Kemal M
Conference : The 1993 Conference on Information Sciences and Systems pp. 812-817
Date: March 01 - March 01, 1993
We show the equivalence of two techniques of time series modeling/prediction; (ii) perceptron learning of probability distribution of the truth value of a proposition from first order stochastic density approximations, (ii) Maximum Partial Likelihood (MPL) estimation of the parameters of a logistic regressive model for binary time series. This result provides large training set characteristics for the approximate Kullback-Leibler relative entropy learning scheme.