Yann Guermeur

I am currently at the head of the ABC research team of the LORIA-UMR 7503 in Nancy
Fédération Charles Hermite
"Directeur de recherche" CNRS
"Notice de titres et travaux"

PhD Students

Hafida Bouziane-Chouarfia (2008-2014)
Rémi Bonidal (2009-2013)
Mounia Hendel (2010-2017)
Edouard Klein (2011-2013)
Khadija Musayeva (2015-2019)
Aya El Dakdouki (2015-2019)
Antoine Moniot (2019-2022)
Tom Masini (2021-2024)

Events

ABC scientific day 2023
Conférence IA et entreprises 2021

Research Projects and Working Groups

PASCAL 2
Project "Artificial Intelligence against Heart Diseases" (AIHD)

Software

WW-M-SVM
LLW-M-SVM
M-SVM-2
MLP
PLR
CC
MSVMpack

MSVMpred

Teaching

Multi-Class Support Vector Machines. Summer School NN2008, Porto.
Protein Secondary Structure Prediction with Multi-Class Support Vector Machines. Summer School NN2008, Porto.

Some papers

Full publication list

Dissertations

SVM Multiclasses, Théorie et Applications. Y. Guermeur (2007). HDR, Université Nancy 1.

Combinaison de classifieurs statistiques, application à la prédiction de la structure secondaire des protéines. Y. Guermeur (1997). PhD thesis, Université Paris 6.

Book chapters

A generic approach to biological sequence segmentation problems: application to protein secondary structure prediction. Y. Guermeur et F. Lauer (2016). In M. Elloumi, C.S. Iliopoulos, J.T.L. Wang and A.Y. Zomaya, editors, Pattern Recognition in Computational Molecular Biology: Techniques and Approaches, Chap. 7, 114-128, Wiley.

Estimation et contrôle des performances en généralisation des réseaux de neurones. Y. Guermeur and O. Teytaud (2006). In Y. Bennani, editor, Apprentissage Connexionniste, Chap. 10, 279-342, Hermès.

A kernel for protein secondary structure prediction. Y. Guermeur, A. Lifchitz and R. Vert (2004). In B. Schölkopf, K. Tsuda and J.-P. Vert, editors, Kernel Methods in Computational Biology, Chap. 9, 193-206, the MIT Press.

Théorie de l'apprentissage de Vapnik et SVM, Support Vector Machines. Y. Guermeur and H. Paugam-Moisy (1999). In M. Sebban and G. Venturini, editors, Apprentissage Automatique, 109-138, Hermès.

International journals with review

ProtNAff: Protein-bound Nucleic Acid filters and fragment libraries. A. Moniot, Y. Guermeur, S. de Vries and I. Chauvot de Beauchêne (2022). Bioinformatics, Vol. 38, N. 16, 3911-3917.

Rademacher complexity of margin multi-category classifiers. Y. Guermeur (2020). Neural Computing and Applications, Vol. 32, 17995-18008.

Rademacher complexity and generalization performance of multi-category margin classifiers. K. Musayeva, F. Lauer and Y. Guermeur (2019). Neurocomputing, Vol. 342, 6-15.

L_p-norm Sauer-Shelah lemma for margin multi-category classifiers. Y. Guermeur (2017). Journal of Computer and System Sciences (JCSS), Vol. 89, 450-473.

Comments on: Support vector machines maximizing geometric margins for multi-class classification. Y. Guermeur (2014). TOP, Vol. 22, N. 3, 844-851.

Model selection for the l₂-SVM by following the regularization path. R. Bonidal, S. Tindel, and Y. Guermeur (2014). Transactions on Computational Collective Intelligence (TCCI), Vol. XIII (LNCS 8342), 83-112.

Combining multi-class SVMs with linear ensemble methods that estimate the class posterior probabilities. Y. Guermeur (2013). Communications in Statistics - Theory and Methods, Vol. 42, N. 16, 3011-3030.

A generic model of multi-class support vector machine. Y. Guermeur (2012). International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 6, N. 6, 555-577.

Hybrid feature selection and SVM-based classification for mouse skin precancerous stages diagnosis from bimodal spectroscopy. F. Abdat, M. Amouroux, Y. Guermeur, and W. Blondel (2012). Optics Express, Vol. 20, N. 1, 228-244.

MSVMpack: a multi-class support vector machine package. F. Lauer and Y. Guermeur (2011). Journal of Machine Learning Research (JMLR), Vol. 12, 2293-2296.

A quadratic loss multi-class SVM for which a radius-margin bound applies. Y. Guermeur and E. Monfrini (2011). INFORMATICA, Vol. 22, N. 1, 73-96.

Sample complexity of classifiers taking values in R^Q, application to multi-class SVMs. Y. Guermeur (2010). Communications in Statistics - Theory and Methods, Vol. 39, N. 3, 543-557.

HECTAR: A method to predict subcellular targeting in heterokonts. B. Gschloessl, Y. Guermeur and J.M. Cock (2008). BMC Bioinformatics, Vol. 9, 393.

VC theory of large margin multi-category classifiers. Y. Guermeur (2007). JMLR, Vol. 8, 2551-2594.

Prediction of amphipathic in-plane membrane anchors in monotopic proteins using a SVM classifier. N. Sapay, Y. Guermeur and G. Deléage (2006). BMC Bioinformatics, Vol. 7, 255.

A comparative study of multi-class support vector machines in the unifying framework of large margin classifiers. Y. Guermeur, A. Elisseeff and D. Zelus (2005). Applied Stochastic Models in Business and Industry (ASMBI), Vol. 21, N. 2, 199-214.

Combining protein secondary structure prediction models with ensemble methods of optimal complexity. Y. Guermeur, G. Pollastri, A. Elisseeff, D. Zelus, H. Paugam-Moisy and P. Baldi (2004). Neurocomputing, Vol. 56, 305-327.

Combining discriminant models with new multi-class SVMs. Y. Guermeur (2002). Pattern Analysis and Applications (PAA), Vol. 5, N. 2, 168-179.

Improved performance in protein secondary structure prediction by inhomogeneous score combination. Y. Guermeur, C. Geourjon, P. Gallinari and G. Deléage (1999). Bioinformatics, Vol. 15, N. 5, 413-421.

Twelve numerical, symbolic and hybrid supervised classification methods. O. Gascuel et al. (1998). International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), Vol. 12, N. 5, 517-571.

National journals with review

Etude comparée des performances de SVM multi-classes en prédiction de la structure secondaire des protéines. Y. Guermeur (2009). Revue des Nouvelles Technologies de l'Information (RNTI), Vol. A-3, 21-48.

Théorie de l'apprentissage de Vapnik et SVM, Support Vector Machines. Y. Guermeur and H. Paugam-Moisy (1999). Revue Electronique sur l'Apprentissage par les Données (READ), Vol. 3, N. 1, 17-38.

Refereed international conference papers

Generalization bounds for inductive matrix completion in low-noise settings. A. Ledent, R. Alves, Y. Lei, Y. Guermeur and M. Kloft (2023). AAAI-23, Washington, 8447-8455.

Inferring epsilon-nets of Finite Sets in a RKHS. A. Moniot, I. Chauvot de Beauchêne and Y. Guermeur (2022). WSOM+ 22, Prague, 53-62.

Rademacher complexity of margin multi-category classifiers. Y. Guermeur (2017). WSOM+ 17, Nancy.

Cascading discriminant and generative models for protein secondary structure prediction. F. Thomarat, F. Lauer, and Y. Guermeur (2012). PRIB'12, Tokyo, 166-177.

Estimating the class posterior probabilities in biological sequence segmentation. R. Bonidal, F. Thomarat, and Y. Guermeur (2012). SMTDA'12, Chania.

Estimating the class posterior probabilities in protein secondary structure prediction. Y. Guermeur and F. Thomarat (2011). PRIB'11, Delft, 260-271.

DCT-SVM based multi-classification of mouse skin precancerous stages from autofluorescence and diffuse reflectance spectra. F. Abdat, M. Amouroux, Y. Guermeur and W. Blondel (2011). ECBO'11, Munich.

Ensemble Methods of Appropriate Capacity for Multi-Class Support Vector Machines. Y. Guermeur (2010). SMTDA'10, Chania, 311-318.

Radius-Margin Bound on the Leave-One-Out Error of the LLW-M-SVM. Y. Guermeur and E. Monfrini (2009). ASMDA'09, Vilnius, 517-521.

Scale-sensitive Psi-dimensions: the Capacity Measures for Classifiers Taking Values in R^Q. Y. Guermeur (2007). ASMDA'07, Chania.

Model selection for multi-class SVMs. Y. Guermeur, M. Maumy and F. Sur (2005). ASMDA'05, Brest, 507-517.

Bound on the risk for M-SVMs. Y. Guermeur, A. Elisseeff and D. Zelus (2002). Statistical Learning, Theory and Applications, Paris, 48-52.

A new multi-class SVM based on a uniform convergence result. Y. Guermeur, A. Elisseeff and H. Paugam-Moisy (2000). IJCNN'00, Come, Vol. IV, 183-188.

Generalization performance of multi-class discriminant models. H. Paugam-Moisy, A. Elisseeff and Y. Guermeur (2000). IJCNN'00, Come, Vol. IV, 177-182.

Estimating the sample complexity of a multi-class discriminant model. Y. Guermeur, A. Elisseeff and H. Paugam-Moisy (1999). ICANN'99, Edimbourg, 310-315.

Multivariate Linear Regression on Classifier Outputs: a Capacity Study. Y. Guermeur, H. Paugam-Moisy and P. Gallinari (1998). ICANN'98, Skövde, 693-698.

Optimal Linear Regression on Classifier Outputs. Y. Guermeur, F. d'Alché-Buc and P. Gallinari (1997). ICANN'97, Lausanne, 481-486.

Combining Statistical Models for Protein Secondary Structure Prediction. Y. Guermeur and P. Gallinari (1996). ICANN'96, Bochum, 599-604.

Refereed national conference papers

Méthode hiérarchique hybride de prédiction des angles de torsion ω des protéines. Y. Guermeur and T. Malliavin (2024). SFC'24, Marseile.

Feature extraction for the clustering of small 3D structures: application to RNA fragments. A. Delannoy, A. Moniot, Y. Guermeur and I. Chauvot de Beauchêne (2021). JOBIM'21, Paris.

Docking of RNA hairpin on protein using a fragment-based method. A. Moniot, R. Roy, Y. Guermeur and I. Chauvot de Beauchêne (2020). JOBIM'20, Montpellier, 88-92.

Approach based on artificial neural networks for protein secondary structure prediction. H. Bouziane-Chouarfia, B. Messabih, Y. Guermeur and A. Chouarfia (2009). NTICRI'09, Oran.

Borne "rayon-marge" sur l'erreur "leave-one-out" des SVM multi-classes. Y. Darcy, E. Monfrini and Y. Guermeur (2006). JdS'06, Clamart.

Prediction of in-plane amphipathic membrane segments based on an SVM method. N. Sapay, Y. Guermeur and G. Deléage (2005). JOBIM'05, Lyon, 299-311.

Traitement Statistique des Résultats de SELEX. D. Eveillard and Y. Guermeur (2002). JOBIM'02, Saint-Malo, 277-283.

Combining protein secondary structure prediction models with ensemble methods of optimal complexity. Y. Guermeur and D. Zelus (2001). JOBIM'01, Toulouse, 97-104.

Risque garanti pour les modèles de discrimination multi-classes. A. Elisseeff, H. Paugam-Moisy and Y. Guermeur (1999). SFC'99, Nancy, 111-118.

Combinaison de classifieurs estimant les probabilités a posteriori des classes. Y. Guermeur (1998). SFC'98, Montpellier, 121-124.

Combinaison Linéaire Optimale de Classifieurs. Y. Guermeur, F. d'Alché-Buc and P. Gallinari (1997). JdS'97, Carcassonne, 425-428.

Research reports

Combinatorial and Structural Results for gamma-Psi-dimensions. Y. Guermeur (2020). arXiv Research Report arXiv:1809.07310.

L_p-norm Sauer-Shelah Lemma for Margin Multi-category Classifiers. Y. Guermeur (2016). arXiv Research Report arXiv:1609.07953.

Radius-Margin Bound on the Leave-One-Out Error of the LLW-M-SVM. Y. Guermeur and E. Monfrini (2009). Research Report LORIA.

A Quadratic Loss Multi-Class SVM. E. Monfrini and Y. Guermeur (2008). Research Report LORIA, hal-00276700.

Radius-margin Bound on the Leave-one-out Error of Multi-class SVMs. Y. Darcy and Y. Guermeur (2005). Research Report INRIA, RR-5780.

Large Margin Multi-category Discriminant Models and Scale-sensitive Psi-dimensions. Y. Guermeur (2004). Research Report INRIA, RR-5314 (revised in 2006).

Recherche des gènes d'ARN non codant. E. Gothié, Y. Guermeur, S. Muller, C. Branlant and A. Bockmayr (2003). Research Report INRIA, RR-5057.

A Simple Unifying Theory of Multi-Class Support Vector Machines. Y. Guermeur (2002). Research Report INRIA, RR-4669.

Bounding the Capacity Measure of Multi-Class Discriminant Models. Y. Guermeur, A. Elisseeff and D. Zelus (2002). Technical Report NeuroCOLT2, 2002-123.

Combining discriminant models with new multi-class SVMs. Y. Guermeur (2000). Technical Report NeuroCOLT2, 2000-086.

Margin error and generalization capabilities of multi-class discriminant systems. A. Elisseeff, Y. Guermeur and H. Paugam-Moisy (1999). Technical Report NeuroCOLT2, 1999-051.

Margin error and generalization capabilities of multi-class discriminant systems. Revised manuscript (draft 06-01).

Linear Ensemble Methods for Multiclass Discrimination. Y. Guermeur and H. Paugam-Moisy (1998). Research Report 1998-52, LIP, ENS Lyon.

Miscellaneous

New cascade architecture for protein secondary structure prediction. Y. Guermeur and F. Thomarat (2010). Abstract of a poster presented at JOBIM'10, Montpellier.

Statistical Processing of SELEX Results. D. Eveillard and Y. Guermeur (2002). Abstract of a poster presented at ISMB'2002, Edmonton.

Combining Protein Secondary Structure Prediction Methods with a new Multi-Category SVM. Y. Guermeur and D. Zelus (2000). Abstract of a poster presented at ISMB'2000, San Diego.

An Ensemble Method for Protein Secondary Structure Prediction. Y. Guermeur, F. d'Alché-Buc and P. Gallinari (1997). Abstract of an oral presentation at MABS'97, Rouen.

Yann Guermeur

Last modified 10-10-2024

e-mail: Yann.Guermeur@loria.fr