Discriminative Learning with Markov Logic Networks
TEXAS UNIV AT AUSTIN DEPT OF COMPUTER SCIENCES
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Statistical relational learning SRL is an emerging area of research that addresses the problem of learning from noisy structuredrelational 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. Most of the existing learning algorithms for MLNs are in the generative setting they try to learn a model that maximizes the likelihood of the training data. However, most of the learning problems in relational data are discriminative. So to utilize the power of MLNs, we need discriminative learning methods that well match these discriminative tasks. In this proposal, we present two new discriminative learning algorithms for MLNs. The first one is a discriminative structure and weight learner for MLNs with non-recursive clauses. We use a variant of ALEPH, an off-the-shelf Inductive Logic Programming ILP system, to learn a large set of Horn clauses from the training data, then we apply an L1-regularization weight learner to select a small set of non-zero weight clauses that maximizes the conditional log-likelihood CLL of the training data. The experimental results show that our proposed algorithm outperforms existing learning methods for MLNs and traditional ILP systems in term of predictive accuracy, and its performance is comparable to stateof-the-art results on some ILP benchmarks. The second algorithm we present is a max-margin weight learner for MLNs. Instead of maximizing the CLL of the data like all existing discriminative weight learners for MLNs, the new weight learner tries to maximize the ratio between the probability of the correct label the observable data and and the closest incorrect label among all the wrong labels, this one has the highest probability, which can be formulated as an optimization problem called 1-slack structural SVM.
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