An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Improved approximation algorithms for MAX SAT
Journal of Algorithms
Training products of experts by minimizing contrastive divergence
Neural Computation
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Discrete Applied Mathematics
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Machine Learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
New developments in parsing technology
An improved analysis of Goemans and Williamson's LP-relaxation for MAX SAT
Theoretical Computer Science - Foundations of computation theory (FCT 2003)
Structured Prediction, Dual Extragradient and Bregman Projections
The Journal of Machine Learning Research
Margin-based first-order rule learning
Machine Learning
Training structural SVMs when exact inference is intractable
Proceedings of the 25th international conference on Machine learning
Discriminative structure and parameter learning for Markov logic networks
Proceedings of the 25th international conference on Machine learning
Efficient Weight Learning for Markov Logic Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Discriminative Structure Learning of Markov Logic Networks
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
The Journal of Machine Learning Research
Learning CRFs Using Graph Cuts
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Cutting-plane training of structural SVMs
Machine Learning
Discriminative training of Markov logic networks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Joint inference in information extraction
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Lifted first-order belief propagation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Probabilistic inductive logic programming
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Generative Structure Learning for Markov Logic Networks
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Discriminative Markov logic network structure learning based on propositionalization and X2-test
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Collective graph identification
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Online structure learning for Markov logic networks
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
On the combination of logical and probabilistic models for information analysis
Applied Intelligence
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Discriminative learning of first-order weighted abduction from partial discourse explanations
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Statistical relational data integration for information extraction
RW'13 Proceedings of the 9th international conference on Reasoning Web: semantic technologies for intelligent data access
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Markov logic networks (MLNs) are an expressive representation for statistical relational learning that generalizes both first-order logic and graphical models. 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 a 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). To train the proposed model, we design a new approximation algorithm for loss-augmented inference in MLNs based on Linear Programming (LP). The experimental result shows that the proposed approach generally achieves higher F 1 scores than the current best discriminative weight learner for MLNs.