Faïcel Chamroukhi (fchamroukhi)

fchamroukhi

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Company:IRT SystemX / UniCaen, CNRS

Location:Palaiseau, France

Home Page:chamroukhi.com

Twitter:@chamroukhif

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Faïcel Chamroukhi's repositories

SaMUraiS

StAtistical Models for the UnsupeRvised segmentAion of tIme-Series

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FLaMingos

Functional Latent datA Models for clusterING heterogeneOus curveS

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HMMR

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

MHMMR

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

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MEteorits

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

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MHMMR_m

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

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mixRHLP_py

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

tMoE_m

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

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mixHMM

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

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mixHMMR

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

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mixRHLP

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

MixRHLP_m

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

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PWR_m

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

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RHLP_m

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

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StMoE

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.

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DECT-CLUST

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

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HMMR_r

Hidden Markov Model Regression (HMMR) for Times Series Segmentation

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MRHLP

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

MRHLP_m

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

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PWR_R

Piecewise Regression (PWR) for Optimal Time Series Segmentation

RHLP

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

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SNMoE

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

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SNMoE_m

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

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StMoE_m

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

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tMoE

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

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HDME

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

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MPWR

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

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MPWR_r

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

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uReMix_m

Unsupervised REgression MIXtures (uReMix)

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uReMix_R

Unsupervised REgression MIXtures (uReMix)

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