Semi-supervised learning is an active research field. Previous results shown that unite background information into the original unsupervised clustering problem could archive higher accuracy. In this paper, we explore the cooperation between the pairwise constrains given by the user and the sematic information in natural language. In addition, we reduce the time complexity to make the algorithm...

Considerable effort has been made to increase the scale of Linked Data. However, because of the openness of the Semantic Web and the ease of extracting Linked Data from semi-structured sources (e.g., Wikipedia) and unstructured sources, many Linked Data sources often provide conflicting objects for a certain predicate of a real-world entity. Existing methods cannot be trivially extended to reso...

A statistical clustering approach is proposed, based on Markov random field models. A discrete field derived from the raw data set is considered as a field of measures. A hidden field, computed using a new potential function, is used to detect the modes that correspond to domains of high local concentrations of observations. Results obtained on artificially generated and real data sets demonstr...

Building higher-dimensional copulas is generally recognized as a difficult problem. Regular-vines using bivariate copulas provide a flexible class of high-dimensional dependency models. In large dimensions, the drawback of the model is the exponentially increasing complexity. Recognizing some of the conditional independences is a possibility for reducing the number of levels of the pair-copula ...

We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their relative merits and limitations remain somewhat obscure. By analyzing a number of concrete examples, we show that low-complexity algorithms systematically fail when the Markov random field develops l...

Trust is an important element of achieving secure collaboration that deals with human judgment and decision making. We consider trust as it arises in and influences people-driven service engagements. Existing approaches for estimating trust between people suffer from two important limitations. One, they consider only commitment as the primary means of estimating trust and omit additional signif...

Gauss–Markov random fields (GMrf’s) play an important role in the modeling of physical phenomena. The paper addresses the second-order characterization and the sample path description of GMrf’s when the indexing parameters take values in bounded subsets of <; d 1. Using results of Pitt, we give conditions for the covariance of a GMrf to be the Green’s function of a partial differential operator...

Service accessibility is defined as the access of a community to the nearby site locations in a service network consisting of multiple geographically distributed service sites. Leveraging new statistical methods, this paper estimates and classifies service accessibility patterns varying over a large geographic area (Georgia) and over a period of 16 years. The focus of this study is on financial...

Repeat-pass SAR interferometric data are multitemporal and display changes occuring between two acquisitions. As a consequence, phase and correlation images contains meaningful informations usable for cropland monitoring. This paper proposes a statistical model to segment high phasimetric structures. It is expressed in a Markov random field framework by using cooperatively phase and correlation...

Markov logic networks (MLNs) use first-order formulas to define features of Markov networks. Current MLN structure learners can only learn short clauses (4-5 literals) due to extreme computational costs, and thus are unable to represent complex regularities in data. To address this problem, we present LSM, the first MLN structure learner capable of efficiently and accurately learning long claus...