Online Max-Margin Weight Learning for Markov Logic Networks

Most of the existing weight-learning algorithms for Markov Logic Networks (MLNs) use batch training which becomes computationally expensive and even infeasible for very large datasets since the training examples may not fit in main memory. To overcome this problem, previous work has used online learning algorithms to learn weights for MLNs. However, this prior work has only applied existing online algorithms, and there is no comprehensive study of online weight learning for MLNs. In this paper, we derive a new online algorithm for structured prediction using the primal-dual framework, apply it to learn weights for MLNs, and compare against existing online algorithms on three large, real-world datasets. The experimental results show that our new algorithm generally achieves better accuracy than existing methods, especially on noisy datasets