%0 Conference Paper %1 phillips_maximum_2004 %A Phillips, Steven J. %A Dudík, Miroslav %A Schapire, Robert E. %B Proceedings of the twenty-first international conference on Machine learning %C New York, NY, USA %D 2004 %I ACM %K distribution entropy, maximum modelling species %P 83-- %R 10.1145/1015330.1015412 %T A maximum entropy approach to species distribution modeling %U http://doi.acm.org/10.1145/1015330.1015412 %X 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. %@ 1-58113-838-5

@inproceedings{phillips_maximum_2004, abstract = {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.}, added-at = {2017-01-09T13:57:26.000+0100}, address = {New York, NY, USA}, author = {Phillips, Steven J. and Dudík, Miroslav and Schapire, Robert E.}, biburl = {https://www.bibsonomy.org/bibtex/240d97010bb00e66ef93b952f21cc794a/yourwelcome}, booktitle = {Proceedings of the twenty-first international conference on {Machine} learning}, doi = {10.1145/1015330.1015412}, interhash = {7a0caa9e9c44056363e38a39c11ba9d8}, intrahash = {40d97010bb00e66ef93b952f21cc794a}, isbn = {1-58113-838-5}, keywords = {distribution entropy, maximum modelling species}, pages = {83--}, publisher = {ACM}, series = {{ICML} '04}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {A maximum entropy approach to species distribution modeling}, url = {http://doi.acm.org/10.1145/1015330.1015412}, urldate = {2012-03-11}, year = 2004 }