The Earth Mover's Distance (EMD) is a robust similarity measure between two histograms (e.g., probability distributions). It has been extensively used in a wide range of applications, e.g., multimedia, data mining, computer vision, etc. As EMD is a computationally intensive operation, many efficient lower and upper bound functions of EMD have been developed. However, they provide no guarantee on the error. In this work, we study how to compute approximate EMD value with bounded error. First, we develop a parametric dual bound function for EMD, in order to offer sufficient trade-off points for optimization. After that, we propose an approximation framework that leverages on lower and upper bound functions to compute approximate EMD with error guarantee. Then, we present three solutions to solve our problem. Experimental results on real data demonstrate the efficiency and the effectiveness of our proposed solutions.