The Maximum Entropy maxent principle has been successfully applied in classi cation and tagging tasks Compared with other statistical learning method it allows convenient integration of di erent knowledge sources However it is restricted by the size of the training corpus in that not all knowledge can be incorporated For instance events observed with low counts can be either a particular patter...

A Bayesian/Maximum entropy (MaxEnt) method is applied to quantify the broadening of Xray line profiles in terms of the nanocrystallite size effects in ceria. The analysis is in general agreement with transmission electron microscopy results, while demonstrating the importance of appropriate a priori information needed in the method. The analysis also identifies other microstructural effects, su...

The asymptotic convergence of probability density function (pdf) and convergence of differential entropy are examined for the non-stationary processes that follow the maximum entropy principle (MaxEnt) and maximum entropy production principle (MEPP). Asymptotic convergence of pdf provides new justification of MEPP while convergence of differential entropy is important in asymptotic analysis of ...

In many practical situations, the Maximum Entropy (MaxEnt) approach leads to reasonable distributions. However, in an important case when all we know is that the value of a random variable is somewhere within the interval, this approach leads to a uniform distribution on this interval – while our intuition says that we should have a distribution whose probability density tends to 0 when we appr...

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...

Named Entity Recognition (NER) is an important task in information extraction, where major attention has been paid to written texts of a news or academic paper (esp. biomedical) style. In this paper we report the first piece of work on NER in spoken Chinese dialogues, as a preliminary step for spoken language understanding. The NER task is taken as a sequential classification problem and solved...

This paper presents an unsupervised method that uses limited amount of labeled data and a large pool of unlabeled data to improve natural language call routing performance. The method uses multiple classifiers to select a subset of the unlabeled data to augment limited labeled data. We evaluated four widely used text classification algorithms; Naive Bayes Classification (NBC), Support Vector ma...

We propose a novel reordering model for phrase-based statistical machine translation (SMT) that uses a maximum entropy (MaxEnt) model to predicate reorderings of neighbor blocks (phrase pairs). The model provides content-dependent, hierarchical phrasal reordering with generalization based on features automatically learned from a real-world bitext. We present an algorithm to extract all reorderi...