Fine-grained ship category recognition is a data-hungry learning task that requires a lot of labeled data which are usually scarce. Alternative models, as transductive attributes, bypass this limitation by considering not only labeled data but also abundant unlabeled ones. However, these transductive methods are basically designed for observed data, and their extension to unobserved sets requires retraining the whole models. In this paper, we introduce a novel ship category recognition method based on semi-supervised learning; the strength of our method resides in its ability to leverage labeled and unlabeled observed data while being highly effective and efficient in order to handle unobserved ones. We consider two variants of our method, the first one is non-parametric and based on support vector regression while the second one is parametric and based on deep neural networks. Experiments conducted on the challenging fine-grained ship category recognition show that our semi-supervised method is highly effective and generalizes well across unobserved sets.