Maximum Entropy (MaxEnt) language models [1, 2] are linear models that are typically regularized via well-known L1 or L2 terms in the likelihood objective, hence avoiding the need for the kinds of backoff or mixture weights used in smoothed ngram language models using Katz backoff [3] and similar techniques. Even though backoff cost is not required to regularize the model, we investigate the us...

The main objective of this paper is to investigate the effectiveness of two recently popular non-parametric models for aboveground biomass (AGB) retrieval from Synthetic Aperture Radar (SAR) L-band backscatter intensity and coherence images. An area in Siberian boreal forests was selected for this study. The results demonstrated that relatively high estimation accuracy can be obtained at a spat...

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...

Training conditional maximum entropy models on massive data sets requires significant computational resources. We examine three common distributed training methods for conditional maxent: a distributed gradient computation method, a majority vote method, and a mixture weight method. We analyze and compare the CPU and network time complexity of each of these methods and present a theoretical ana...

Maximum entropy inverse reinforcement learning (MaxEnt IRL) is an effective approach for learning the underlying rewards of demonstrated human behavior, while it is intractable in high-dimensional state space due to the exponential growth of calculation cost. In recent years, a few works on approximating MaxEnt IRL in large state spaces by graphs provide successful results, however, types of st...

Data on molecularly identified adult and larval mosquitoes collected from 104 sites from the Republic of Korea (ROK) in 2007 were used to test the predictive ability of recently reported ecological niche models (ENMs) for 8 potential malaria vectors. The ENMs, based on the program Maxent and the least presence threshold criterion, predicted 100% of new collection locations for Anopheles sinensi...

We investigate the task of medical concept coreference resolution in clinical text using two semi-supervised methods, co-training and multi-view learning with posterior regularization. By extracting semantic and temporal features of medical concepts found in clinical text, we create conditionally independent data views; co-training MaxEnt classifiers on this data works almost as well as supervi...

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...

The task of inferring the native language of an author based on texts written in a second language has generally been tackled as a classification problem, typically using as features a mix of n-grams over characters and part of speech tags (for small and fixed n) and unigram function words. To capture arbitrarily long n-grams that syntax-based approaches have suggested are useful, adaptor gramm...