Erving Goffman, one of the most influential sociologists of the last century published the Presentation of Self in Everyday Life (PSEL;1959). This is the most definitive 20th century study of the patterns of human behavior in mundane social situations. If we look to psychology, sociology, anthropology, marketing or even organisational studies the representation of human behavior emerges from ri...

This paper proposes an approach to improve statistical word alignment with ensemble methods. Two ensemble methods are investigated: bagging and cross-validation committees. On these two methods, both weighted voting and unweighted voting are compared under the word alignment task. In addition, we analyze the effect of different sizes of training sets on the bagging method. Experimental results ...

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Neural network ensembles are applied to handwritten digit recognition. The invidual networks of the ensemble are combinations of sparse Look-Up Tables with random receptive fields. It is shown that the consensus of a group of networks outperform the best invidual of the ensemble and further we show that it is possible to estimate the ensemble performance as well as the learning curve, on a medi...

As a result of the lack of the knowledge with regard to the statistical properties of the dynamic models and operational observations, as well as the computational burden related to the high dimensionality of the realistic data assimilation problems especially those complex nonlinear filtering problems, the ensemble Kalman filter scheme has been paid much more attention in recent years and has ...

Ensemble methods have been shown to be an effective tool for solving multi-label classification tasks. In the RAndom k-labELsets (RAKEL) algorithm, each member of the ensemble is associated with a small randomly-selected subset of k labels. Then, a single label classifier is trained according to each combination of elements in the subset. In this paper we adopt a similar approach, however, inst...

Ensemble methods have been receiving an increasing amount of attention, especially because of their successful application to high visibility problems (e.g., the NetFlix prize). An important challenge in ensemble learning (EL) is the management of the set of models to ensure a high level of accuracy, particularly with large number of models and in highly dynamic environments [49]. One approach ...

Background and Objectives: According to the random nature of heuristic algorithms, stability analysis of heuristic ensemble classifiers has particular importance. Methods: The novelty of this paper is using a statistical method consists of Plackett-Burman design, and Taguchi for the first time to specify not only important parameters, but also optimal levels for them. Minitab and Design Expert ...