The incorporation of the Random Vector Functional Link (RVFL) concept into mixture models for predicting conditional probability densities achieves a considerable speed-up of the training process. This allows the creation of a large ensemble of predictors, which results in an improvement in the generalization performance .

We present a Gaussian Mixture model for detecting different types of figurative language in context. We show that this model performs well when the parameters are estimated in an unsupervised fashion using EM. Performance can be improved further by estimating the parameters from a small annotated data set.

Contents 1 Introduction 2 The R program 3 Phenology, an introduction 4 Geo-statistical modelling & analysis of phenological phases 5 Multivariate tools to visualise spatio-temporal patterns in phenology 6 Gaussian Mixture Models of geographical phenomena 7 Analysis of space-time correlations within geographical studies

The Dirichlet process prior allows flexible nonparametric mixture modeling. The number of mixture components is not specified in advance and can grow as new data come in. However, the behavior of the model is sensitive to the choice of the parameters, including an infinite-dimensional distributional parameter G0. Most previous applications have either fixed G0 as a member of a parametric family...

When reviewing his clinical experience in treating suicidal patients, one of the authors observed that successful predictions of suicidality were often based on the patient’s voice independent of content. Using the Gaussian mixture model to represent the mel-cepstral features of voiced speech, speech of suicidal persons can be distinguish from that of depressed and control persons. The question...

A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allows us to summarize the properties of the high-dimensional clusters (or density...

In the mixture distribution model (for continuous and discrete cases) the density function, f̃ (t) is given by a linear combination of k density functions, fi(t), i= 1, · · · ,k, with non-negative weights pi which must sum to 1.0. We propose a generalization of the mixture model where the weights are not restricted to being constant. The specification of the weight functions is not easy because ...

We explore a recently proposed mixture model approach to understanding interactions between conflicting sensory cues. Alternative model formulations, differing in their sensory noise models and inference methods, are compared based on their fit to experimental data. Heavy-tailed sensory likelihoods yield a better description of the subjects’ response behavior than standard Gaussian noise models...

revision date: May 2008 This abstract was prepared by the principal investigator for the project. Please see www.americanchemistry.com/lri for more information about the LRI.