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Designing a classifier in the absence of labeled data is becoming a common encounter as the acquisition of informative labels is often difficult or expensive, particularly on new uncharted target domains. The feasibility of attaining a reliable classifier for the task of interest is embarked by some in transfer learning, where label information from relevant source domains is considered for complimenting the design process. The core challenge arising from such endeavors, however, is the induction of source sample selection bias, such that the trained classifier has the tendency of steering toward the distribution of the source domain. In addition, this bias is deemed to become more severe on data involving multiple classes. Considering this cue, our interest in this paper is to address such a challenge in the target domain, where ordinal labeled data are unavailable. In contrast to the previous works, we propose a transfer ordinal label learning paradigm to predict the ordinal labels of target unlabeled data by spanning the feasible solution space with ensemble of ordinal classifiers from the multiple relevant source domains. Specifically, the maximum margin criterion is considered here for the construction of the target classifier from an ensemble of source ordinal classifiers. Theoretical analysis and extensive empirical studies on real-world data sets are presented to study the benefits of the proposed method.