Max-Margin Weight Learning for Markov Logic Networks.
| Tuyen Ngoc Huynh | University of Texas, Austin | [Home Page] |
Notice: Hosted by Rodrigo Braz
Date: Thursday June 17, 2010 at 16:00
Location: EJ228 (SRI E building). slides via WebEx from 3:45pm on, sound at 1-888-355-1249, 749045 (Directions)
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Statistical relational learning (SRL) is an emerging area of research that addresses the problem of learning from noisy structured/relational data. Markov logic networks (MLNs), sets of weighted clauses, are a simple but powerful SRL formalism that combines the expressivity of first-order logic with the flexibility of probabilistic reasoning. Existing discriminative weight learning methods for MLNs all try to learn weights that optimize the Conditional Log Likelihood (CLL) of the training examples. In this work, we present a new discriminative weight learning method for MLNs based on the max-margin framework. This results in a new model, Max-Margin Markov Logic Networks (M3LNs), that combines the expressiveness of MLNs with the predictive accuracy of structural Support Vector Machines (SVMs). We developed algorithms to train the proposed model on both batch and online setting. The experimental results on real-world datatsets show that the proposed approach achieves better accuracy than existing ones. |
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