Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Machine Learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Training structural SVMs when exact inference is intractable
Proceedings of the 25th international conference on Machine learning
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Cutting-plane training of structural SVMs
Machine Learning
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
A new class of upper bounds on the log partition function
IEEE Transactions on Information Theory
MAP estimation via agreement on trees: message-passing and linear programming
IEEE Transactions on Information Theory
Fast Structured Prediction Using Large Margin Sigmoid Belief Networks
International Journal of Computer Vision
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Boltzmann Machines are a powerful class of undirected graphical models. Originally proposed as artificial neural networks, they can be regarded as a type of Markov Random Field in which the connection weights between nodes are symmetric and learned from data. They are also closely related to recent models such as Markov logic networks and Conditional Random Fields. A major challenge for Boltzmann machines (as well as other graphical models) is speeding up learning for large-scale problems. The heart of the problem lies in efficiently and effectively approximating the partition function. In this paper, we propose a new efficient learning algorithm for Boltzmann machines that allows them to be applied to problems with large numbers of random variables. We introduce a new large-margin variational approximation to the partition function that allows Boltzmann machines to be trained using a support vector machine (SVM) style learning algorithm. For discriminative learning tasks, these large margin Boltzmann machines provide an alternative approach to structural SVMs. We show that these machines have low sample complexity and derive a generalization bound. Our results demonstrate that on multilabel classification problems, large margin Boltzmann machines achieve orders of magnitude faster performance than structural SVMs and also outperform structural SVMs on problems with large numbers of labels.