Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
On the convergence of the coordinate descent method for convex differentiable minimization
Journal of Optimization Theory and Applications
A maximum entropy approach to natural language processing
Computational Linguistics
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
Sequential conditional Generalized Iterative Scaling
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A Fast Dual Algorithm for Kernel Logistic Regression
Machine Learning
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Accelerated training of conditional random fields with stochastic gradient methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Scalable training of L1-regularized log-linear models
Proceedings of the 24th international conference on Machine learning
An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression
The Journal of Machine Learning Research
Surrogate maximization/minimization algorithms and extensions
Machine Learning
Trust Region Newton Method for Logistic Regression
The Journal of Machine Learning Research
Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines
The Journal of Machine Learning Research
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
The Journal of Machine Learning Research
Iterative scaling and coordinate descent methods for maximum entropy
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
The Journal of Machine Learning Research
The Latent Maximum Entropy Principle
ACM Transactions on Knowledge Discovery from Data (TKDD)
An improved GLMNET for L1-regularized logistic regression
The Journal of Machine Learning Research
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Maximum entropy (Maxent) is useful in natural language processing and many other 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 iterative scaling methods. This framework also connects iterative scaling and coordinate descent methods. We prove general convergence results for IS methods and analyze their computational complexity. Based on the proposed framework, we extend a coordinate descent method for linear SVM to Maxent. Results show that it is faster than existing iterative scaling methods.