Faïcel Chamroukhi

Hidden Markov Model Regression (HMMR) for Times Series Segmentation

Hidden Markov Model Regression (HMMR) for time-series segmentation

StAtistical Models for the UnsupeRvised segmentAion of tIme-Series

Robust Mixtures-of-Experts modelling using the t distribution for clustering and non-linear regression for heteregenous data

Mixtures-of-ExperTs modEling for cOmplex and non-noRmal dIsTributionS

Joint segmentation of multivariate time-series with a Multiple Hidden Markov Model Regression (MHMMR)

High-Dimensional Mixtures-of-Experts: proximal Newton EM for estimation and feature selection in high-dimensional mixtures of generalised linear experts models

A flexible mixture model for simultaneous clustering and segmentation of functional data (time series). It uses the EM algorithm (or a CEM-like algorithm).

Piecewise regression (PWR) for the optimal segmentation of time-series with regime changes

Joint segmentation of multivariate time series with a Multiple Hidden Markov Model Regression (MHMMR)

Robust modeling, density estimation and model-based clustering of heterogeneous regression data with possibly skewed and non-normal distributions using skew-t mixture of experts.

DECT-CLUST: DECT image clustering and application to HNSCC tumor segmentation

Clustering and segmentation of time series with regime changes by mixture of Hidden Markov Model Regressions (MixFHMMR) and the EM algorithm

Clustering and segmentation of heterogeneous functional data (sequential data) with regime changes by mixture of Hidden Markov Model Regressions (MixFHMMR) and the EM algorithm

Clustering and segmentation of time series by mixture of gaussian Hidden Markov Models (MixFHMMs) and the EM algorithm

Toolbox for the Skew-t mixture of experts (StMoE) model

Functional Latent datA Models for clusterING heterogeneOus curveS

A flexible mixture model for simultaneous clustering and segmentation of functional data (time series). It uses the EM algorithm (or a CEM-like algorithm).

Flexible Mixture modelling for simultaneous clustering and segmentation of heterogeneous functional data

Joint segmentation of multivariate time-series with a Multiple Piece-Wise Regression model (MPWR)

Clustering and segmentation of heteregeneous functional data (sequential data) by mixture of gaussian Hidden Markov Models (MixFHMMs) and the EM algorithm

Unsupervised REgression MIXtures (uReMix)

Unsupervised REgression MIXtures (uReMix)

Robust Mixtures-of-Experts for Non-Linear Regression and Clustering

Skew-Normal Mixture-of-Experts: A toolbox for Non-Linear Regression and Clustering using some non-normal mixtures of experts

User-freindly and flexible algorithm for time series segmentation by a Regression model with a Hidden Logistic Process (RHLP).

Joint segmentation of multivariate time series with a Multiple Regression model with a Hidden Logistic Process (MRHLP).

Skew-Normal Mixture-of-Experts: A toolbox for Non-Linear Regression and Clustering using some non-normal mixtures of experts

Piecewise Regression (PWR) for Optimal Time Series Segmentation

Optimal joint segmentation of multivariate time-series with a Multiple Piece-Wise Regression model (MPWR)