On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
An introduction to variational methods for graphical models
Learning in graphical models
Proceedings of the 1998 conference on Advances in neural information processing systems II
Training products of experts by minimizing contrastive divergence
Neural Computation
Learning Probabilistic Models of Relational Structure
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A New Learning Algorithm for Mean Field Boltzmann Machines
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Dynamic Trees: A Structured Variational Method Giving Efficient Propagation Rules
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Image Modeling with Position-Encoding Dynamic Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Web data extraction based on partial tree alignment
WWW '05 Proceedings of the 14th international conference on World Wide Web
The Journal of Machine Learning Research
Dynamic Trees for Unsupervised Segmentation and Matching of Image Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchical Field Framework for Unified Context-Based Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
2D Conditional Random Fields for Web information extraction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Simultaneous record detection and attribute labeling in web data extraction
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Corrective feedback and persistent learning for information extraction
Artificial Intelligence
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An overlapping tree approach to multiscale stochastic modeling and estimation
IEEE Transactions on Image Processing
Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction
The Journal of Machine Learning Research
Document structure meets page layout: loopy random fields for web news content extraction
Proceedings of the 10th ACM symposium on Document engineering
A unified approach for extracting multiple news attributes from news pages
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
A comparison of discriminative classifiers for web news content extraction
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Extracting multiple news attributes based on visual features
Journal of Intelligent Information Systems
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Hierarchical models have been extensively studied in various domains. However, existing models assume fixed model structures or incorporate structural uncertainty generatively. In this paper, we propose Dynamic Hierarchical Markov Random Fields (DHMRFs) to incorporate structural uncertainty in a discriminative manner. DHMRFs consist of two parts -- structure model and class label model. Both are defined as exponential family distributions. Conditioned on observations, DHMRFs relax the independence assumption as made in directed models. As exact inference is intractable, a variational method is developed to learn parameters and to find the MAP model structure and label assignment. We apply the model to a real-world web data extraction task, which automatically extracts product items for sale on the Web. The results show promise.