Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European 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
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Table extraction using conditional random fields
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Nomograms for visualizing support vector machines
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Predicting protein folds with structural repeats using a chain graph model
ICML '05 Proceedings of the 22nd international conference on Machine learning
International Journal of Computer Vision
Semi-supervised learning for structured output variables
ICML '06 Proceedings of the 23rd international conference on Machine learning
Kernel-Based Learning of Hierarchical Multilabel Classification Models
The Journal of Machine Learning Research
Structured Prediction, Dual Extragradient and Bregman Projections
The Journal of Machine Learning Research
Transductive support vector machines for structured variables
Proceedings of the 24th international conference on Machine learning
Exact and Approximate Inference for Annotating Graphs with Structural SVMs
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Learning to Combine Bottom-Up and Top-Down Segmentation
International Journal of Computer Vision
Boosting Protein Threading Accuracy
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
Web Page Prediction Based on Conditional Random Fields
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
A Generalization of Forward-Backward Algorithm
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Structured prediction by joint kernel support estimation
Machine Learning
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
The Journal of Machine Learning Research
Minimizing and learning energy functions for side-chain prediction
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Semi-supervised sequence classification using abstraction augmented Markov models
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Semi-supervised ranking for document retrieval
Computer Speech and Language
Learning from partially annotated sequences
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Expansion finding for given acronyms using conditional random fields
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Conditional graphical models for protein structure prediction
Conditional graphical models for protein structure prediction
Learning conditional random fields with latent sparse features for acronym expansion finding
Proceedings of the 20th ACM international conference on Information and knowledge management
Semi-supervised multi-task learning of structured prediction models for web information extraction
Proceedings of the 20th ACM international conference on Information and knowledge management
Multi-view discriminative sequential learning
ECML'05 Proceedings of the 16th European conference on Machine Learning
Efficient classification of images with taxonomies
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
On Taxonomies for Multi-class Image Categorization
International Journal of Computer Vision
Kernel conditional ordinal random fields for temporal segmentation of facial action units
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Learning to predict from textual data
Journal of Artificial Intelligence Research
One-class conditional random fields for sequential anomaly detection
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Machine Vision and Applications
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Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models is given which shows how kernel conditional random fields arise from risk minimization procedures defined using Mercer kernels on labeled graphs. A procedure for greedily selecting cliques in the dual representation is then proposed, which allows sparse representations. By incorporating kernels and implicit feature spaces into conditional graphical models, the framework enables semi-supervised learning algorithms for structured data through the use of graph kernels. The framework and clique selection methods are demonstrated in synthetic data experiments, and are also applied to the problem of protein secondary structure prediction.