Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Kernels for Semi-Structured Data
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
An Alternate Objective Function for Markovian Fields
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Text classification using string kernels
The Journal of Machine Learning Research
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
A hybrid Japanese parser with hand-crafted grammar and statistics
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Investigating loss functions and optimization methods for discriminative learning of label sequences
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Extraction of Windows in Facade Using Kernel on Graph of Contours
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Improving sequence segmentation learning by predicting trigrams
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
A graph matching method and a graph matching distance based on subgraph assignments
Pattern Recognition Letters
Windows and facades retrieval using similarity on graph of contours
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Indexing of 3d models based on graph of surfacic regions
Proceedings of the ACM workshop on 3D object retrieval
Inexact graph matching based on kernels for object retrieval in image databases
Image and Vision Computing
Structured representations in a content based image retrieval context
Journal of Visual Communication and Image Representation
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We introduce a new perceptron-based discriminative learning algorithm for labeling structured data such as sequences, trees, and graphs. Since it is fully kernelized and uses pointwise label prediction, large features, including arbitrary number of hidden variables, can be incorporated with polynomial time complexity. This is in contrast to existing labelers that can handle only features of a small number of hidden variables, such as Maximum Entropy Markov Models and Conditional Random Fields. We also introduce several kernel functions for labeling sequences, trees, and graphs and efficient algorithms for them.