AIC Seminar Series
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 17th 2010 at 4:00pm
Location: EJ228 (SRI E building). slides via WebEx from 3:45pm on, sound at 1-888-355-1249, 749045 (Directions)
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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