Semi‐supervised logistic discrimination via labeled data and unlabeled data from different sampling distributions

摘要

Abstract 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 expectation and maximization (EM) algorithm. A crucial issue in th.