Speaker: Robert Schapire, Princeton University
Abstract:
Modeling the geographic distribution of a plant or animal species is a critical problem in conservation biology: to save a threatened species, one first needs to know where it prefers to live, and what its requirements are for survival. From a machine-learning perspective, this is an especially challenging problem in which the learner is presented with no negative examples and often only a tiny number of positive examples. In this talk, I will describe the application of maximum-entropy methods to this problem, a set of decades-old techniques that happen to fit the problem very cleanly and effectively. I will describe a version of maxent that we have shown enjoys strong theoretical performance guarantees that enable it to perform effectively even with a very large number of features. I will also describe some extensive experimental tests of the method, as well as some surprising applications.
This talk includes joint work with Miroslav DudÃk and Steven Phillips.
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