A maximum entropy approach to natural language processing
Computational Linguistics
Maximum entropy model-based baseball highlight detection and classification
Computer Vision and Image Understanding - Special issue on event detection in video
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Detecting anomalies in network traffic using maximum entropy estimation
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
A novel framework and training algorithm for variable-parameter hidden Markov models
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Link between copula and tomography
Pattern Recognition Letters
Joint estimation of confidence and error causes in speech recognition
Speech Communication
Hi-index | 0.10 |
We investigate the problem of using continuous features in the maximum entropy (MaxEnt) model. We explain why the MaxEnt model with the moment constraint (MaxEnt-MC) works well with binary features but not with the continuous features. We describe how to enhance constraints on the continuous features and show that the weights associated with the continuous features should be continuous functions instead of single values. We propose a spline-based solution to the MaxEnt model with non-linear continuous weighting functions and illustrate that the optimization problem can be converted into a standard log-linear model at a higher-dimensional space. The empirical results on two classification tasks that contain continuous features are reported. The results confirm our insight and show that our proposed solution consistently outperforms the MaxEnt-MC model and the bucketing approach with significant margins.