This article addresses error detection in broadcast news automatic transcription, as a post-processing stage. Based on the observation that many errors appear in bursts, we investigated the use of Markov Chains (MC) for their temporal modelling capabilities. Experiments were conducted on a large Amercian English broadcast news corpus from NIST. Common features in error detection were used, all ...

SDMtoolbox 2.0 is a software package for spatial studies of ecology, evolution, and genetics. The release of SDMtoolbox 2.0 allows researchers to use the most current ArcGIS software and MaxEnt software, and reduces the amount of time that would be spent developing common solutions. The central aim of this software is to automate complicated and repetitive spatial analyses in an intuitive graph...

Insect pests are a major threat to agricultural biosecurity across the world, causing substantial economic losses. Majority of the species distribution modeling studies use precise coordinates (latitude/longitude) of species occurrences in MaxEnt (or maximum entropy model). However, lack of precise coordinates of insect pest occurrences at national/regional level is a common problem for many co...

We compare and contrast two different models for detecting sentence-like units in continuous speech, using both acoustic and lexical information. The first approach is based on hidden Markov sequence models based on N-grams, uses maximum likelihood estimation, and model interpolation to combine different representations of the data. The second approach models the posterior probabilities of the ...

Graduation of data is of great importance in survival analysis. Smoothness and goodness of fit are two fundamental requirements in graduation. Based on the instinctive defining expression for entropy in terms of a probability distribution, two optimization models based on the Maximum Entropy Principle (MaxEnt) and Minimum Cross Entropy Principle (MinCEnt) to estimate mortality probability distr...

In this work, we are concerned with a coarse grained semantic analysis over sparse data, which labels all nouns with a set of semantic categories. To get the benefit of unlabeled data, we propose a bootstrapping framework with Maximum Entropy modeling (MaxEnt) as the statistical learning component. During the iterative tagging process, unlabeled data is used not only for better statistical esti...

One of the available tools for mapping the geographical distribution and potential suitable habitats is species distribution models. These techniques are very helpful for finding poorly known distributions of species in poorly sampled areas, such as the tropics. Maximum Entropy (MaxEnt) is a recently developed modeling method that can be successfully calibrated using a relatively small number o...

Oriental arborvitae (Platycladus orientalis) is an important afforestation and ornamental tree species, which is native in eastern Asian. Therefore, a global suitable habitat map for oriental arborvitae is urgently needed for global promotion and cultivation. Here, the potential habitat and climatic requirements of oriental arborvitae at global scale were simulated using herbariums data and 13 ...

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...