Abstract To writing anything on a keyboard at all requires us to know first what type, then activate motor programmes for finger movements, and execute these. An interruption in the information flow any of these stages leads disfluencies. capture this combination fluent typing hesitations, researchers calculate different measures from keystroke-latency data—such as mean inter-keystroke interval...

In this paper, we introduce and evaluate a data-driven staged mixture modeling tech­ nique for building density, regression, and classification models. Our basic approach is to sequentially add components to a finite mixture model using the structural expec­ tation maximization (SEM) algorithm. We show that our technique is qualitatively sim­ ilar to boosting. This correspondence is a natural b...

In Image Processing Model Based Image Segmentation plays a dominant role in Image Analysis and Image Retrieval . Recently much work has been reported regarding Image Segmentation based on Finite Gaussian Mixture Models using EM algorithm. (Yiming Wu et al (2003)) , (Yamazaki.T (1998)). However, in some images the pixel intensities inside the image regions may not be MesoKurtic or Bell Shaped, b...

A Bayesian nonparametric method for unimodal densities on the real line is provided by considering a class of species sampling mixture models containing random densities that are unimodal and not necessarily symmetric. This class of densities generalize the model considered by Brunner (1992), in which the Dirichlet process is replaced by a more general class of species sampling models. A novel ...

Nevertheless the widespread application of finite mixture models in segmentation, finite mixture model selection is still an important issue. In fact, the selection of an adequate number of segments is a key issue in deriving latent segments structures and it is desirable that the selection criteria used for this end are effective. In order to select among several information criteria, which ma...

Modeling across site variation of the substitution process is increasingly recognized as important for obtaining more accurate phylogenetic reconstructions. Both finite and infinite mixture models have been proposed and have been shown to significantly improve on classical single-matrix models. Compared with their finite counterparts, infinite mixtures have a greater expressivity. However, they...

In this paper, we investigate employment of discriminatively trained acoustic features modeled by Subspace Gaussian Mixture Models (SGMMs) for Rich Transcription meeting recognition. More specifically, first, we focus on exploiting various types of complex features estimated using neural network combined with conventional cepstral features and modeled by standard HMM/GMMs and SGMMs. Then, outpu...

Word rates in text vary according to global factors such as genre, topic, author, and expected readership (Church and Gale 1995). Models that summarize such global factors in text or at the document level, are called ‘text models.’ A finite mixture of Dirichlet distribution (Dirichlet Mixture or DM for short) was investigated as a new text model. When parameters of a multinomial are drawn from ...

A mathematical model considering mass and momentum transfer was developed for simulation of ethanol dewatering via pervaporation process. The process involves removal of water from a water/ethanol liquid mixture using a dense polymeric membrane. The model domain was divided into two compartments including feed and membrane. For a description of water transport in ...

BACKGROUND . When data on preferences are not available, analysts rely on condition-specific or generic measures of health status like the SF-12 for predicting or mapping preferences. Such prediction is challenging because of the characteristics of preference data, which are bounded, have multiple modes, and have a large proportion of observations clustered at values of 1. METHODS . We develo...