Abstract Multi-view learning receives increasing interest in recent years to analyze complex data. Lately, multiview maximum entropy discrimination (MVMED) and alternative MVMED (AMVMED) were proposed as extensions of maximum entropy discrimination (MED) to the multi-view learning setting, which use the hard margin consistency principle that enforces two view margins to be the same. In this paper, we propose soft margin consistency based multi-view MED (SMVMED) achieving margin consistency in a less strict way, which minimizes the relative entropy between the posteriors of two view margins. With a trade-off parameter balancing large margin and margin consistency, SMVMED is more flexible. We also propose a sequential minimal optimization (SMO) algorithm to efficiently train SMVMED and make it scalable to large datasets. We evaluate the performance of SMVMED on multiple real-world datasets and get encouraging results.