172 citations · 664 across the 34 of their papers we have counts for
10 papers · 1 filter
Functional mixture-of-experts for classification
Nhat Thien Pham, Faicel Chamroukhi
We develop a mixtures-of-experts (ME) approach to the multiclass classification where the predictors are univariate functions. It consists of a ME model in which both the gating ne…
Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models
Faïcel Chamroukhi, Florian Lecocq, Hien D. Nguyen
Mixtures-of-Experts models and their maximum likelihood estimation (MLE) via the EM algorithm have been thoroughly studied in the statistics and machine learning literature. They a…
Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models
Faicel Chamroukhi, Bao-Tuyen Huynh
Mixture of Experts (MoE) are successful models for modeling heterogeneous data in many statistical learning problems including regression, clustering and classification. Generally…
Model-Based Clustering and Classification of Functional Data
Faicel Chamroukhi, Hien D. Nguyen
The problem of complex data analysis is a central topic of modern statistical science and learning systems and is becoming of broader interest with the increasing prevalence of hig…
An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling
Hien D. Nguyen, Faicel Chamroukhi
Mixture-of-experts (MoE) models are a powerful paradigm for modeling of data arising from complex data generating processes (DGPs). In this article, we demonstrate how different Mo…
Dirichlet Process Parsimonious Mixtures for clustering
Faicel Chamroukhi, Marius Bartcus, Hervé Glotin
The parsimonious Gaussian mixture models, which exploit an eigenvalue decomposition of the group covariance matrices of the Gaussian mixture, have shown their success in particular…
Model-based clustering with Hidden Markov Model regression for time series with regime changes
Faicel Chamroukhi, Allou Samé, Patrice Aknin +1
This paper introduces a novel model-based clustering approach for clustering time series which present changes in regime. It consists of a mixture of polynomial regressions governe…
Supervised learning of a regression model based on latent process. Application to the estimation of fuel cell life time
Raïssa Onanena, Faicel Chamroukhi, Latifa Oukhellou +3
This paper describes a pattern recognition approach aiming to estimate fuel cell duration time from electrochemical impedance spectroscopy measurements. It consists in first extrac…
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression
Dorra Trabelsi, Samer Mohammed, Faicel Chamroukhi +2
Using supervised machine learning approaches to recognize human activities from on-body wearable accelerometers generally requires a large amount of labelled data. When ground trut…
Joint segmentation of multivariate time series with hidden process regression for human activity recognition
Faicel Chamroukhi, Samer Mohammed, Dorra Trabelsi +2
The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as he…