6 citations · 13 across the 5 of their papers we have counts for
5 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…