Parallel and Deterministic Algorithms from MRFs: Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving the mean field approximation via the use of mixture distributions
Learning in graphical models
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Estimating the Pen Trajectories of Static Signatures Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hierarchical Bayesian language model based on Pitman-Yor processes
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A second-order HMM for high performance word and phoneme-based continuous speech recognition
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
TSVM-HMM: Transductive SVM based hidden Markov model for automatic image annotation
Expert Systems with Applications: An International Journal
A stochastic memoizer for sequence data
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A new distance measure for hidden Markov models
Expert Systems with Applications: An International Journal
The infinite hidden Markov random field model
IEEE Transactions on Neural Networks
Communications of the ACM
The echo state conditional random field model for sequential data modeling
Expert Systems with Applications: An International Journal
IEEE Transactions on Fuzzy Systems
The mean field theory in EM procedures for blind Markov random field image restoration
IEEE Transactions on Image Processing
Hi-index | 12.05 |
Generative models for sequential data are usually based on the assumption of temporal dependencies described by a first-order Markov chain. To ameliorate this shallow modeling assumption, several authors have proposed models with higher-order dependencies. However, the practical applicability of these approaches is hindered by their prohibitive computational costs in most cases. In addition, most existing approaches give rise to model training algorithms with objective functions that entail multiple spurious local optima, thus requiring application of tedious countermeasures to avoid getting trapped to bad model estimates. In this paper, we devise a novel margin-maximizing model with convex objective function that allows for capturing infinitely-long temporal dependencies in sequential datasets. This is effected by utilizing a recently proposed nonparametric Bayesian model of label sequences with infinitely-long temporal dependencies, namely the sequence memoizer, and training our model using margin maximization and a versatile mean-field-like approximation to allow for increased computational efficiency. As we experimentally demonstrate, the devised margin-maximizing construction of our model, which leads to a convex optimization scheme, without any spurious local optima, combined with the capacity of our model to capture long and complex temporal dependencies, allow for obtaining exceptional pattern recognition performance in several applications.