Abstract: A distribution of an unobserved class for a classifier with no known training data is learned by first determining, for each known class, known distribution using known training data. Sufficient statistics of the distribution of the unobserved class are determined from the known distributions and the training data associated with each known class. If the known training data and the known distributions are bounded, then update parameters of the distribution of the unobserved class from the sufficient statistics, else update the parameters from sufficient statistics and a priori probability distributions that specify the distributions of the parameters.

Abstract: A distribution of an unobserved class for a classifier with no known training data is learned by first determining, for each known class, known distribution using known training data. Sufficient statistics of the distribution of the unobserved class are determined from the known distributions and the training data associated with each known class. If the known training data and the known distributions are bounded, then update parameters of the distribution of the unobserved class from the sufficient statistics, else update the parameters from sufficient statistics and a priori probability distributions that specify the distributions of the parameters.