Type:
Conference Paper›Invited and refereed articles in conference proceedings
Authored by:
Sonmez, Kemal M., Baras, John S.
Conference date:
March 1993
Conference:
The 1993 Conference on Information Sciences and Systems, pp. 812-817
Full Text Paper:
Abstract:
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.