Additive Kernel SVM has been extensively used in many applications, including human activity detection and pedestrian detection. Since training an additive kernel SVM model is very time-consuming, which is not scalable to large-scale datasets, many efficient solutions have been developed in the past few years. However, most of the existing methods normally fail to achieve one of these three important conditions which are (1) low classification error, (2) low memory space, and (3) low training time. In order to simultaneously fulfill these three conditions, we develop the new piecewise-linear approximate measure (PLAME) for additive kernels. By incorporating PLAME with the well-known dual coordinate descent method, we theoretically show that this approach can achieve the above three conditions. Experimental results on twelve real datasets show that our approach can achieve the best trade-off between the accuracy, memory space, and training time compared with different types of state-of-the-art methods.