Faïcel Chamroukhi's Projects
DECT-CLUST: DECT image clustering and application to HNSCC tumor segmentation
Functional Latent datA Models for clusterING heterogeneOus curveS
High-Dimensional Mixtures-of-Experts: proximal Newton EM for estimation and feature selection in high-dimensional mixtures of generalised linear expert models
Hidden Markov Model Regression (HMMR) for time-series segmentation
Hidden Markov Model Regression (HMMR) for Times Series Segmentation
Mixtures-of-ExperTs modEling for cOmplex and non-noRmal dIsTributionS
Joint segmentation of multivariate time series with a Multiple Hidden Markov Model Regression (MHMMR)
Joint segmentation of multivariate time-series with a Multiple Hidden Markov Model Regression (MHMMR)
Clustering and segmentation of time series by mixture of gaussian Hidden Markov Models (MixFHMMs) and the EM algorithm
Clustering and segmentation of heteregeneous functional data (sequential data) by mixture of gaussian Hidden Markov Models (MixFHMMs) and the EM algorithm
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
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
A flexible mixture model for simultaneous clustering and segmentation of functional data (time series). It uses the EM algorithm (or a CEM-like algorithm).
Optimal joint segmentation of multivariate time-series with a Multiple Piece-Wise Regression model (MPWR)
Joint segmentation of multivariate time-series with a Multiple Piece-Wise Regression model (MPWR)
Joint segmentation of multivariate time series with a Multiple Regression model with a Hidden Logistic Process (MRHLP).
Joint segmentation of multivariate time-series with a Multiple Regression model with a Hidden Logistic Process (MRHLP).
Piecewise regression (PWR) for the optimal segmentation of time-series with regime changes
Piecewise Regression (PWR) for Optimal Time Series Segmentation
User-freindly and flexible algorithm for time series segmentation by a Regression model with a Hidden Logistic Process (RHLP).
User-friendly and flexible algorithm for time-series segmentation by a Regression model with a Hidden Logistic Process (RHLP).
StAtistical Models for the UnsupeRvised segmentAion of tIme-Series
Skew-Normal Mixture-of-Experts: A toolbox for Non-Linear Regression and Clustering using some non-normal mixtures of experts
Skew-Normal Mixture-of-Experts: A toolbox for Non-Linear Regression and Clustering using some non-normal mixtures of experts
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.
Toolbox for the Skew-t mixture of experts (StMoE) model
Robust Mixtures-of-Experts for Non-Linear Regression and Clustering
Robust Mixtures-of-Experts modelling using the t distribution for clustering and non-linear regression for heteregenous data