On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Sparse bayesian learning and the relevance vector machine
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
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
International Journal of Computer Vision
Scalable training of L1-regularized log-linear models
Proceedings of the 24th international conference on Machine learning
Cost-sensitive learning with conditional Markov networks
Data Mining and Knowledge Discovery
Robust Sequential Data Modeling Using an Outlier Tolerant Hidden Markov Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Action categorization with modified hidden conditional random field
Pattern Recognition
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Survey: Reservoir computing approaches to recurrent neural network training
Computer Science Review
Margin-maximizing classification of sequential data with infinitely-long temporal dependencies
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Sequential data labeling is a fundamental task in machine learning applications, with speech and natural language processing, activity recognition in video sequences, and biomedical data analysis being characteristic such examples, to name just a few. The conditional random field (CRF), a log-linear model representing the conditional distribution of the observation labels, is one of the most successful approaches for sequential data labeling and classification, and has lately received significant attention in machine learning, as it achieves superb prediction performance in a variety of scenarios. Nevertheless, existing CRF formulations do not account for temporal dependencies between the observed variables - they only postulate Markovian interdependencies between the predicted label variables. To resolve these issues, in this paper we propose a non-linear hierarchical CRF formulation that combines the power of echo state networks to extract high level temporal features with the graphical framework of CRF models, yielding a powerful and scalable probabilistic model that we apply to signal labeling tasks.