The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Hidden Markov models for online classification of single trial EEG data
Pattern Recognition Letters
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Active Hidden Markov Models for Information Extraction
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
The Journal of Machine Learning Research
Learning with segment boundaries for hierarchical HMMs
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Sports video segmentation using a hierarchical hidden CRF
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
On the temporal behavior of EEG recorded during real finger movement
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Hierarchical hidden conditional random fields for information extraction
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Marginalized Viterbi algorithm for hierarchical hidden Markov models
Pattern Recognition
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Hidden Markov Models (HMMs) are very popular generative models for sequence data. Recent work has, however, shown that on many tasks, Conditional Random Fields (CRFs), a type of discriminative model, perform better than HMMs. We propose Hierarchical Hidden Conditional Random Fields (HHCRFs), a discriminative model corresponding to hierarchical HMMs (HHMMs). HHCRFs model the conditional probability of the states at the upper levels given observations. The states at the lower levels are hidden and marginalized in the model definition. We have developed two algorithms for the model: a parameter learning algorithm that needs only the states at the upper levels in the training data and the marginalized Viterbi algorithm, which computes the most likely state sequences at the upper levels by marginalizing the states at the lower levels. In an experiment that involves segmenting electroencephalographic (EEG) data for a Brain-Computer Interface, HHCRFs outperform HHMMs.