@conference {
	title = {Time Series Modeling by Perceptrons: A Likelihood Approach},
	author = {Sonmez, Kemal M., Baras, John S.},
	year = {1993},
	month = {07/1993},
	abstract = {We consider neural network learning problems in which the objective is to learn the relationship between the inputs and the probability distribution of a proposition. We regard successive truth values of the proposition as a dependent binary time series whose instantaneous probability of truth is a function of the past behavior of the joint process between the analog inputs and binary output truth values. In this context, we identify the gradient descent learning algorithm using the Kullback-Leibler relative entropy cost function on a perceptron with a Maximum~Partial Likelihood (MPL) estimator of the perceptron model for the probability of a binary event in terms of its covariates. The implications of this result are: (i) The neural network models obtained by relative entropy learning are shown to have the nice large sample (i.e. training set size) characteristics of MPL estimates: consistency and asymptotic normality. (ii) An important and widely used statistical inference technique, logistic regression, can efficiently be implemented on analog perceptrons for time series modeling and prediction.},
	address = {Portland, Oregon},
	booktitle = {The World Congress on Neural Networks},
	pages = {pp. 601-604}
}
@conference {
	title = {Maximum Partial Likelihood Estimation with Perceptrons},
	author = {Sonmez, Kemal M., Baras, John S.},
	year = {1993},
	month = {03/1993},
	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.},
	address = {Baltimore, Maryland},
	booktitle = { The 1993 Conference on Information Sciences and Systems},
	pages = {pp. 812-817},
	organization = {Johns Hopkins University}
}
