Insufficiency of training data is a major obstacle in machine learning and data mining applications. Many different semi-supervised learning algorithms have been proposed to tackle this difficulty by leveraging a large amount of unlabeled data. However, most of them focus on semi-supervised classification. In this paper we propose a semi-supervised regression algorithm named Semi-Supervised Kernel Regression (SSKR). While classical kernel regression is only based on labeled examples, our approach extends it to all observed examples using a weighting factor to modulate the effect of unlabeled examples. Experimental results prove that SSKR significantly outperforms traditional kernel regression and graph-based semi-supervised regression methods.