Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Spatio-Temporal Unified Model for On-Line Handwritten Chinese Character Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Bayesian Network Modeling of Strokes and their Relationships for On-line Handwriting Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Dependence, correlation and Gaussianity in independent component analysis
The Journal of Machine Learning Research
Handling Spatial Information in On-Line Handwriting Recognition
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Contextual Recognition of Hand-Drawn Diagrams with Conditional Random Fields
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Online Handwritten Shape Recognition Using Segmental Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
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We propose a new framework for the modelling of sequences that generalizes popular models such as hidden Markov models. Our approach relies on the use of relational features that describe relationships between observations in a sequence. The use of such relational features allows implementing a variety of models from traditional Markovian models to richer models that exhibit robustness to various kinds of deformation in the input signal. We derive inference and training algorithms for our framework and provide experimental results on on-line handwriting data. We show how the models we propose may be useful for a variety of traditional tasks such as sequence classification but also for applications more related to diagnosis such as partial matching of sequences.