research
∙
09/16/2023
Data-driven Reachability using Christoffel Functions and Conformal Prediction
An important mathematical tool in the analysis of dynamical systems is t...
share
research
∙
02/28/2022
Functional mixture-of-experts for classification
We develop a mixtures-of-experts (ME) approach to the multiclass classif...
research
∙
02/04/2022
Functional Mixtures-of-Experts
We consider the statistical analysis of heterogeneous data for clusterin...
research
∙
01/31/2022
Spectral image clustering on dual-energy CT scans using functional regression mixtures
Dual-energy computed tomography (DECT) is an advanced CT scanning techni...
research
∙
04/18/2021
Non-asymptotic model selection in block-diagonal mixture of polynomial experts models
Model selection, via penalized likelihood type criteria, is a standard t...
research
∙
04/06/2021
A non-asymptotic penalization criterion for model selection in mixture of experts models
Mixture of experts (MoE) is a popular class of models in statistics and ...
research
∙
12/04/2020
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...
research
∙
09/22/2020
An l_1-oracle inequality for the Lasso in mixture-of-experts regression models
Mixture-of-experts (MoE) models are a popular framework for modeling het...
research
∙
08/22/2020
Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces
The class of location-scale finite mixtures is of enduring interest both...
research
∙
07/14/2019
Estimation and Feature Selection in Mixtures of Generalized Linear Experts Models
Mixtures-of-Experts (MoE) are conditional mixture models that have shown...
research
∙
03/01/2019
Approximation by finite mixtures of continuous density functions that vanish at infinity
Given sufficiently many components, it is often cited that finite mixtur...
research
∙
10/29/2018
Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models
Mixture of Experts (MoE) are successful models for modeling heterogeneou...
research
∙
03/01/2018
Model-Based Clustering and Classification of Functional Data
The problem of complex data analysis is a central topic of modern statis...
research
∙
07/12/2017
An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling
Mixture-of-experts (MoE) models are a powerful paradigm for modeling of ...
research
∙
08/04/2015
Bayesian mixtures of spatial spline regressions
This work relates the framework of model-based clustering for spatial fu...
research
∙
06/22/2015
Non-Normal Mixtures of Experts
Mixture of Experts (MoE) is a popular framework for modeling heterogenei...
research
∙
01/14/2015
Dirichlet Process Parsimonious Mixtures for clustering
The parsimonious Gaussian mixture models, which exploit an eigenvalue de...
research
∙
09/24/2014
Unsupervised learning of regression mixture models with unknown number of components
Regression mixture models are widely studied in statistics, machine lear...
research
∙
12/25/2013
Model-based clustering with Hidden Markov Model regression for time series with regime changes
This paper introduces a novel model-based clustering approach for cluste...
research
∙
12/25/2013
Robust EM algorithm for model-based curve clustering
Model-based clustering approaches concern the paradigm of exploratory da...
research
∙
12/25/2013
Mixture model-based functional discriminant analysis for curve classification
Statistical approaches for Functional Data Analysis concern the paradigm...
research
∙
12/25/2013
Classification automatique de données temporelles en classes ordonnées
This paper proposes a method of segmenting temporal data into ordered cl...
research
∙
12/25/2013
Functional Mixture Discriminant Analysis with hidden process regression for curve classification
We present a new mixture model-based discriminant analysis approach for ...
research
∙
12/25/2013
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...
research
∙
12/25/2013
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...
research
∙
12/25/2013
A regression model with a hidden logistic process for signal parametrization
A new approach for signal parametrization, which consists of a specific ...
research
∙
12/25/2013
Modèle à processus latent et algorithme EM pour la régression non linéaire
A non linear regression approach which consists of a specific regression...
research
∙
12/25/2013
Piecewise regression mixture for simultaneous functional data clustering and optimal segmentation
This paper introduces a novel mixture model-based approach for simultane...
research
∙
12/25/2013
Time series modeling by a regression approach based on a latent process
Time series are used in many domains including finance, engineering, eco...
research
∙
12/25/2013
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...
research
∙
12/25/2013
Model-based clustering and segmentation of time series with changes in regime
Mixture model-based clustering, usually applied to multidimensional data...
research
∙
12/25/2013
Model-based functional mixture discriminant analysis with hidden process regression for curve classification
In this paper, we study the modeling and the classification of functiona...
research
∙
12/25/2013
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression
Using supervised machine learning approaches to recognize human activiti...
research
∙
12/25/2013