Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sequential conditional Generalized Iterative Scaling
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
Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines
The Journal of Machine Learning Research
Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models
The Journal of Machine Learning Research
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Maximum entropy (Maxent) is useful in many areas. Iterative scaling (IS) methods are one of the most popular approaches to solve Maxent. With many variants of IS methods, it is difficult to understand them and see the differences. In this paper, we create a general and unified framework for IS methods. This framework also connects IS and coordinate descent (CD) methods. Besides, we develop a CD method for Maxent. Results show that it is faster than existing iterative scaling methods.