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Steven J. Phillips
Researcher at AT&T Labs
Publications - 69
Citations - 44945
Steven J. Phillips is an academic researcher from AT&T Labs. The author has contributed to research in topics: Competitive analysis & Time complexity. The author has an hindex of 38, co-authored 69 publications receiving 38245 citations. Previous affiliations of Steven J. Phillips include AT&T & Bell Labs.
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Maximum entropy modeling of species geographic distributions
TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.
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Novel methods improve prediction of species' distributions from occurrence data
Jane Elith,Catherine H. Graham,Robert P. Anderson,Miroslav Dudík,Simon Ferrier,Antoine Guisan,Robert J. Hijmans,Falk Huettmann,John R. Leathwick,Anthony Lehmann,Jin Li,Lúcia G. Lohmann,Bette A. Loiselle,Glenn Manion,Craig Moritz,Miguel Nakamura,Yoshinori Nakazawa,Jacob C. M. Mc Overton,A. Townsend Peterson,Steven J. Phillips,Karen Richardson,Ricardo Scachetti-Pereira,Robert E. Schapire,Jorge Soberón,Stephen E. Williams,Mary S. Wisz,Niklaus E. Zimmermann +26 more
TL;DR: This work compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date and found that presence-only data were effective for modelling species' distributions for many species and regions.
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Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
TL;DR: This paper presents a tuning method that uses presence-only data for parameter tuning, and introduces several concepts that improve the predictive accuracy and running time of Maxent and describes a new logistic output format that gives an estimate of probability of presence.
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A statistical explanation of MaxEnt for ecologists
TL;DR: A new statistical explanation of MaxEnt is described, showing that the model minimizes the relative entropy between two probability densities defined in covariate space, which is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts.
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Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data
Steven J. Phillips,Miroslav Dudík,Jane Elith,Catherine H. Graham,Anthony Lehmann,John R. Leathwick,Simon Ferrier +6 more
TL;DR: It is argued that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions and as large an effect on predictive performance as the choice of modeling method.