We develop a maximum entropy (maxent) approach to generating recommendations in the context of a user’s current navigation stream, suitable for environments where data is sparse, high-dimensional, and dynamic— conditions typical of many recommendation applications. We address sparsity and dimensionality reduction by first clustering items based on user access patterns so as to attempt to minimi...

BACKGROUND Predicting species' potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. METHODOLOGY We collected the dis...

BACKGROUND Rare or narrowly endemic organisms are difficult to monitor and conserve when their total distribution and habitat preferences are incompletely known. One method employed in determining distributions of these organisms is species distribution modeling (SDM). METHODS Using two species of narrowly endemic burrowing crayfish species as our study organisms, we sought to ground validate...

BACKGROUND Species distribution models are often used to characterize a species' native range climate, so as to identify sites elsewhere in the world that may be climatically similar and therefore at risk of invasion by the species. This endeavor provoked intense public controversy over recent attempts to model areas at risk of invasion by the Indian Python (Python molurus). We evaluated a numb...

Distribution data on threatened and endangered species are often sparse and clustered making it difficult to model their suitable habitat distribution using commonly used modeling approaches. We used a novel method called maximum entropy distribution modeling or Maxent for predicting potential suitable habitat for Canacomyrica monticola, a threatened and endangered tree species in New Caledonia...

Automatic detection of disfluencies in spoken language is important for making speech recognition output more readable, and for aiding downstream language processing modules. We compare a generative hidden Markov model (HMM)-based approach and two conditional models — a maximum entropy (Maxent) model and a conditional random field (CRF) — for detecting disfluencies in speech. The conditional mo...

BACKGROUND Plague, caused by the bacterium Yersinia pestis, is a public and wildlife health concern in California and the western United States. This study explores the spatial characteristics of positive plague samples in California and tests Maxent, a machine-learning method that can be used to develop niche-based models from presence-only data, for mapping the potential distribution of plagu...

Recent advances in ecological modeling have focused on novel methods for characterizing the environment that use presence-only data and machine-learning algorithms to predict the likelihood of species occurrence. These novel methods may have great potential for land suitability applications in the developing world where detailed land cover information is often unavailable or incomplete. This pa...