
Nonasymptotic model selection in blockdiagonal mixture of polynomial experts models
Model selection, via penalized likelihood type criteria, is a standard t...
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A nonasymptotic penalization criterion for model selection in mixture of experts models
Mixture of experts (MoE) is a popular class of models in statistics and ...
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Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models
Mixture of experts (MoE) models are widely applied for conditional proba...
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An l_1oracle inequality for the Lasso in mixtureofexperts regression models
Mixtureofexperts (MoE) models are a popular framework for modeling het...
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Approximation of probability density functions via locationscale finite mixtures in Lebesgue spaces
The class of locationscale finite mixtures is of enduring interest both...
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Estimation and Feature Selection in Mixtures of Generalized Linear Experts Models
MixturesofExperts (MoE) are conditional mixture models that have shown...
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Approximation by finite mixtures of continuous density functions that vanish at infinity
Given sufficiently many components, it is often cited that finite mixtur...
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Regularized Maximum Likelihood Estimation and Feature Selection in MixturesofExperts Models
Mixture of Experts (MoE) are successful models for modeling heterogeneou...
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ModelBased Clustering and Classification of Functional Data
The problem of complex data analysis is a central topic of modern statis...
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An Introduction to the Practical and Theoretical Aspects of MixtureofExperts Modeling
Mixtureofexperts (MoE) models are a powerful paradigm for modeling of ...
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Bayesian mixtures of spatial spline regressions
This work relates the framework of modelbased clustering for spatial fu...
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NonNormal Mixtures of Experts
Mixture of Experts (MoE) is a popular framework for modeling heterogenei...
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Dirichlet Process Parsimonious Mixtures for clustering
The parsimonious Gaussian mixture models, which exploit an eigenvalue de...
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Unsupervised learning of regression mixture models with unknown number of components
Regression mixture models are widely studied in statistics, machine lear...
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Modelbased clustering with Hidden Markov Model regression for time series with regime changes
This paper introduces a novel modelbased clustering approach for cluste...
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Robust EM algorithm for modelbased curve clustering
Modelbased clustering approaches concern the paradigm of exploratory da...
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Mixture modelbased functional discriminant analysis for curve classification
Statistical approaches for Functional Data Analysis concern the paradigm...
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Classification automatique de données temporelles en classes ordonnées
This paper proposes a method of segmenting temporal data into ordered cl...
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Functional Mixture Discriminant Analysis with hidden process regression for curve classification
We present a new mixture modelbased discriminant analysis approach for ...
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Supervised learning of a regression model based on latent process. Application to the estimation of fuel cell life time
This paper describes a pattern recognition approach aiming to estimate f...
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A regression model with a hidden logistic process for feature extraction from time series
A new approach for feature extraction from time series is proposed in th...
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A regression model with a hidden logistic process for signal parametrization
A new approach for signal parametrization, which consists of a specific ...
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Modèle à processus latent et algorithme EM pour la régression non linéaire
A non linear regression approach which consists of a specific regression...
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Piecewise regression mixture for simultaneous functional data clustering and optimal segmentation
This paper introduces a novel mixture modelbased approach for simultane...
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Time series modeling by a regression approach based on a latent process
Time series are used in many domains including finance, engineering, eco...
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A hidden process regression model for functional data description. Application to curve discrimination
A new approach for functional data description is proposed in this paper...
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Modelbased clustering and segmentation of time series with changes in regime
Mixture modelbased clustering, usually applied to multidimensional data...
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Modelbased functional mixture discriminant analysis with hidden process regression for curve classification
In this paper, we study the modeling and the classification of functiona...
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An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression
Using supervised machine learning approaches to recognize human activiti...
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Joint segmentation of multivariate time series with hidden process regression for human activity recognition
The problem of human activity recognition is central for understanding a...
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Faicel Chamroukhi
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Full Professor of Statistics and Data Science at Université de Caen Normandie