Online Max-Margin Weight Learning with Markov Logic Networks (2010)
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 new online algorithms for structured prediction using the primaldual framework, apply them to learn weights forMLNs, and compare against existing online algorithms on two large, real-world datasets. The experimental results show that the new algorithms achieve better accuracy than existing methods.
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In Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10), pp. 32--37, Atlanta, GA, July 2010.
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Tuyen N. Huynh Ph.D. Alumni hntuyen [at] cs utexas edu
Raymond J. Mooney Faculty mooney [at] cs utexas edu