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Emmanuel Monfrini
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2020 – today
- 2023
- [j13]Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski:
Equivalence between LC-CRF and HMM, and Discriminative Computing of HMM-Based MPM and MAP. Algorithms 16(3): 173 (2023) - [i9]Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski:
Linear chain conditional random fields, hidden Markov models, and related classifiers. CoRR abs/2301.01293 (2023) - 2022
- [c24]Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski:
On Equivalence between Linear-chain Conditional Random Fields and Hidden Markov Chains. ICAART (3) 2022: 725-728 - [c23]Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski:
Improving Usual Naive Bayes Classifier Performances with Neural Naive Bayes based Models. ICPRAM 2022: 315-322 - [i8]Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski:
Deriving discriminative classifiers from generative models. CoRR abs/2201.00844 (2022) - 2021
- [j12]Hugo Gangloff, Jean-Baptiste Courbot
, Emmanuel Monfrini, Christophe Collet:
Unsupervised image segmentation with Gaussian Pairwise Markov Fields. Comput. Stat. Data Anal. 158: 107178 (2021) - [c22]Elie Azeraf
Highly Fast Text Segmentation With Pairwise Markov Chains. CIST 2021: 361-366 - [c21]Clément Fernandes, Thomas Monti, Emmanuel Monfrini, Wojciech Pieczynski:
Fast Image Segmentation with Contextual Scan and Markov Chains. EUSIPCO 2021: 626-630 - [c20]Elie Azeraf, Emmanuel Monfrini, Emmanuel Vignon, Wojciech Pieczynski:
Introducing the Hidden Neural Markov Chain Framework. ICAART (2) 2021: 1013-1020 - [c19]Hugo Gangloff, Jean-Baptiste Courbot, Emmanuel Monfrini, Christophe Collet:
Unsupervised Image Segmentation with Spatial Triplet Markov Trees. ICASSP 2021: 1790-1794 - [c18]Elie Azeraf
Using the Naive Bayes as a discriminative model. ICMLC 2021: 106-110 - [i7]Elie Azeraf, Emmanuel Monfrini, Emmanuel Vignon, Wojciech Pieczynski:
Highly Fast Text Segmentation With Pairwise Markov Chains. CoRR abs/2102.11037 (2021) - [i6]Elie Azeraf, Emmanuel Monfrini, Emmanuel Vignon, Wojciech Pieczynski:
Introducing the Hidden Neural Markov Chain framework. CoRR abs/2102.11038 (2021) - [i5]Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski:
Improving usual Naive Bayes classifier performances with Neural Naive Bayes based models. CoRR abs/2111.07307 (2021) - [i4]Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski:
On equivalence between linear-chain conditional random fields and hidden Markov chains. CoRR abs/2111.07376 (2021) - 2020
- [c17]Hugo Gangloff, Emmanuel Monfrini, Christophe Collet, Nabil Chakfé:
Unsupervised segmentation of stents corrupted by artifacts in medical X-ray images. IPTA 2020: 1-6 - [c16]Hugo Gangloff, Emmanuel Monfrini, Mohamed Zied Ghariani, Mickaël Ohana, Christophe Collet, Nabil Chakfé:
Improved centerline tracking for new descriptors of atherosclerotic aortas. IPTA 2020: 1-6 - [i3]Elie Azeraf, Emmanuel Monfrini, Emmanuel Vignon, Wojciech Pieczynski:
Hidden Markov Chains, Entropic Forward-Backward, and Part-Of-Speech Tagging. CoRR abs/2005.10629 (2020) - [i2]Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski:
Using the Naive Bayes as a discriminative classifier. CoRR abs/2012.13572 (2020)
2010 – 2019
- 2019
- [j11]Jean-Baptiste Courbot
Pairwise Markov fields for segmentation in astronomical hyperspectral images. Signal Process. 163: 41-48 (2019) - 2018
- [j10]Jean-Baptiste Courbot
Oriented Triplet Markov Fields. Pattern Recognit. Lett. 103: 16-22 (2018) - [j9]Ivan Gorynin, Hugo Gangloff, Emmanuel Monfrini, Wojciech Pieczynski:
Assessing the segmentation performance of pairwise and triplet Markov models. Signal Process. 145: 183-192 (2018) - [c15]Jean-Baptiste Courbot
Triplet Markov Trees for Image Segmentation. SSP 2018: 233-237 - 2017
- [j8]Ivan Gorynin, Stéphane Derrode
Fast smoothing in switching approximations of non-linear and non-Gaussian models. Comput. Stat. Data Anal. 114: 38-46 (2017) - [j7]Jean-Baptiste Courbot
Extended faint source detection in astronomical hyperspectral images. Signal Process. 135: 274-283 (2017) - [j6]Ivan Gorynin, Stéphane Derrode
Fast Filtering in Switching Approximations of Nonlinear Markov Systems With Applications to Stochastic Volatility. IEEE Trans. Autom. Control. 62(2): 853-862 (2017) - [c14]Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski:
Pairwise Markov models for stock index forecasting. EUSIPCO 2017: 2041-2045 - [c13]Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski:
Unsupervised learning of asymmetric high-order autoregressive stochastic volatility model. ICASSP 2017: 4780-4784 - [c12]Mohamed El Yazid Boudaren, Emmanuel Monfrini, Kadda Beghdad Bey, Ahmed Habbouchi
Triplet Markov Chains Based- Estimation of Nonstationary Latent Variables Hidden with Independent Noise. ICEIS (Revised Selected Papers) 2017: 127-144 - [c11]Mohamed El Yazid Boudaren, Emmanuel Monfrini, Kadda Beghdad Bey, Ahmed Habbouchi, Wojciech Pieczynski:
Unsupervised Segmentation of Nonstationary Data using Triplet Markov Chains. ICEIS (1) 2017: 405-414 - 2016
- [j5]Meriem Yahiaoui, Emmanuel Monfrini, Bernadette Dorizzi:
Markov Chains for unsupervised segmentation of degraded NIR iris images for person recognition. Pattern Recognit. Lett. 82: 116-123 (2016) - [c10]Jean-Baptiste Courbot, Vincent Mazet, Emmanuel Monfrini, Christophe Collet:
Detection of faint extended sources in hyperspectral data and application to HDF-S MUSE observations. ICASSP 2016: 1891-1895 - [c9]Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski:
Unsupervised learning of Markov-switching stochastic volatility with an application to market data. MLSP 2016: 1-6 - [c8]Ivan Gorynin, Emmanuel Monfrini, Wojciech Pieczynski:
Fast filtering with new sparse transition Markov chains. SSP 2016: 1-5 - [c7]Jean-Baptiste Courbot, Emmanuel Monfrini, Vincent Mazet, Christophe Collet:
Oriented Triplet Markov fields for hyperspectral image segmentation. WHISPERS 2016: 1-5 - 2015
- [c6]Ivan Gorynin, Stéphane Derrode, Emmanuel Monfrini, Wojciech Pieczynski:
Exact fast smoothing in switching models with application to stochastic volatility. EUSIPCO 2015: 924-928 - [c5]Jean-Baptiste Courbot
2-Step robust vertebra segmentation. IPTA 2015: 157-162 - 2014
- [j4]Mohamed El Yazid Boudaren
Phasic Triplet Markov Chains. IEEE Trans. Pattern Anal. Mach. Intell. 36(11): 2310-2316 (2014) - [c4]Meriem Yahiaoui, Emmanuel Monfrini, Bernadette Dorizzi:
Implementation of Unsupervised Statistical Methods for Low-Quality Iris Segmentation. SITIS 2014: 566-573 - 2012
- [j3]Mohamed El Yazid Boudaren
Dempster-Shafer fusion of multisensor signals in nonstationary Markovian context. EURASIP J. Adv. Signal Process. 2012: 134 (2012) - [j2]Mohamed El Yazid Boudaren
Unsupervised Segmentation of Random Discrete Data Hidden With Switching Noise Distributions. IEEE Signal Process. Lett. 19(10): 619-622 (2012) - [c3]Mohamed El Yazid Boudaren, Emmanuel Monfrini, Wojciech Pieczynski:
Unsupervised segmentation of nonstationary pairwise Markov Chains using evidential priors. EUSIPCO 2012: 2243-2247 - 2011
- [j1]Yann Guermeur, Emmanuel Monfrini:
A Quadratic Loss Multi-Class SVM for which a Radius-Margin Bound Applies. Informatica 22(1): 73-96 (2011) - [c2]François Desbouvries, Yohan Petetin, Emmanuel Monfrini:
Optimal SIR algorithm vs. fully adapted auxiliary particle filter: A matter of conditional independence. ICASSP 2011: 3992-3995 - [c1]Mohamed El Yazid Boudaren, Emmanuel Monfrini, Wojciech Pieczynski:
Unsupervised segmentation of switching pairwise Markov chains. ISPA 2011: 183-188
2000 – 2009
- 2008
- [i1]Emmanuel Monfrini, Yann Guermeur:
A Quadratic Loss Multi-Class SVM. CoRR abs/0804.4898 (2008)
Coauthor Index
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