Incomplete data collection in regression analysis would lead to low prediction performance, which aises the issue of domain adaptation. It is well known that support vector regression (SVR) is equivalent to center-constrained minimum enclosing ball (CC-MEB). Also in solving the problem of how to effectively transfer the knowledge between the two fields, new theorems reveal that the difference between two probability distributions from two similar domains only depends on the centers of the two domains' minimum enclosing balls. Based on these developments, a fast adaptive-core vector regression (A-CVR) algorithm is proposed for large domain adaptation. The proposed algorithm uses the center of the source domain's CC-MEB to calibrate the center of the target domain's in order to improve the regression performance of the target domain. Experimental results show that the proposed domain adaptive algorithm can make up for the lack of data and greatly improve the performance of the target domain regression.