Selected papers of the MSSA/IMACS 9th Biennial conference on Modeling and simulation
Bayesian regularization and pruning using a Laplace prior
Neural Computation
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
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
On the convergence of bound optimization algorithms
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
The maximum entropy method for analyzing retrieval measures
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
An Extension of Iterative Scaling for Decision and Data Aggregation in Ensemble Classification
Journal of VLSI Signal Processing Systems
On-demand set-based recommendations
Proceedings of the fourth ACM conference on Recommender systems
Environmental Modelling & Software
Maximum entropy distribution estimation with generalized regularization
COLT'06 Proceedings of the 19th annual conference on Learning Theory
A Modular Machine Learning System for Flow-Level Traffic Classification in Large Networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Traffic classification combining flow correlation and ensemble classifier
International Journal of Wireless and Mobile Computing
Proceedings of the 23rd international conference on World wide web
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We study the problem of modeling species geographic distributions, a critical problem in conservation biology. We propose the use of maximum-entropy techniques for this problem, specifically, sequential-update algorithms that can handle a very large number of features. We describe experiments comparing maxent with a standard distribution-modeling tool, called GARP, on a dataset containing observation data for North American breeding birds. We also study how well maxent performs as a function of the number of training examples and training time, analyze the use of regularization to avoid overfitting when the number of examples is small, and explore the interpretability of models constructed using maxent.