A priori parameter choice rules are successful in verifying the convergence of the reconstructed solutions. However, they suffer from a major drawback of utilizing source conditions, which, in most cases, is unknown. This pitfall is circumvented by invoking a posteriori parameter choice rules. In this article, we propound and investigate an Engl-type discrepancy principle for the choice of the regularization parameter in the learning theory perspective and establish the convergence rate. The consistency of the algorithm is an easy consequence. Moreover, we provide some insightful discussion on the weighted parameter. From a practical point of view, we demonstrate our theoretical analysis through two well-studied academic examples in learning theory.