Coral reef ecosystems are threatened by both climate change and direct anthropogenic stress. Climate change will alter the physico-chemical environment that reefs currently occupy, leaving only limited regions that are conducive to reef habitation. Identifying these regions early may aid conservation efforts and inform decisions to transplant particular coral species or groups. Here a species d...

Lobesia botrana (Denis & Schiffermüller, 1775) (Lepidoptera: Tortricidae) is one of the most destructive pests of grape in the Palearctic region. The potential geographical distribution of this pest is important to agriculture security. In this study, Maxent and ArcGIS were used to project the potential geographical distribution of L. botrana in China under the current climate. The result indic...

Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development or validation rarely considers functional components of species traits or other types of biologica...

In this paper, we propose a simple approach for approximating small sample distributions based on the method of Maximum Entropy (maxent) density. Unlike previous studies that use the maxent method as a curve-fitting tool, we focus on the “small sample asymptotics” of the approximation. We show that this method obtains the same asymptotic order of accuracy as that of the classical Edgeworth expa...

Sentence boundary detection in speech is important for enriching speech recognition output, making it easier for humans to read and downstream modules to process. In previous work, we have developed hidden Markov model (HMM) and maximum entropy (Maxent) classifiers that integrate textual and prosodic knowledge sources for detecting sentence boundaries. In this paper, we evaluate the use of a co...

Maxent, one of the most commonly used methods for inferring species distributions and environmental tolerances from occurrence data, allows users to fit models of arbitrary complexity. Model complexity is typically constrained via a process known as L1 regularization, but at present little guidance is available for setting the appropriate level of regularization, and the effects of inappropriat...

Statistical modeling of presence-only data has attracted much recent attention in the ecological literature, leading to a proliferation of methods, including the inhomogeneous poisson process (IPP) model [15], maximum entropy (Maxent) modeling of species distributions [12] [9] [10], and logistic regression models. Several recent articles have shown the close relationships between these methods ...

We derive a dual-primal recursive algorithm based on the Fenchel duality framework, extending Dykstra’s successive projections and Csiszar’s I-projections schemes, to handle Tsallis MaxEnt. The Tsallis entropy Sq(p) is a one-parameter extension of Shannon’s entropyH(p) in the sense that Sq→1(p) = H(p). The solution of the Tsallis MaxEnt falls under a q-deformed Gibbs distribution which is a pow...

This paper focuses on verb sense disambiguation cast as inferring the VerbNet class to which a verb belongs. To train three different supervised learning models –Maximum Entropy (MaxEnt), Naive Bayes and Decision Tree– we used lexical, co-occurrence and typed-dependency features. For each model, we built three classifiers: one single classifier for all verbs, one single classifier for polysemou...