  This article addresses the problem of classification method based on both
labeled and unlabeled data, where we assume that a density function for labeled
data is different from that for unlabeled data. We propose a semi-supervised
logistic regression model for classification problem along with the technique
of covariate shift adaptation. Unknown parameters involved in proposed models
are estimated by regularization with EM algorithm. A crucial issue in the
modeling process is the choices of tuning parameters in our semi-supervised
logistic models. In order to select the parameters, a model selection criterion
is derived from an information-theoretic approach. Some numerical studies show
that our modeling procedure performs well in various cases.
