Yann Guermeur
I am currently at the head of the
ABC research team of the
LORIA-UMR 7503
in Nancy
Fédération Charles Hermite
"Chargé de recherche" CNRS
"Notice de titres et travaux"
PhD Students
Hafida Bouziane-Chouarfia (2008-)
Rémi Bonidal (2009-)
Special session "Supervised Prediction with Neural Networks and SVMs" of ASMDA 2007 Conference
Pascal Theoretical Challenge. Type I and type II errors for multiple simultaneous hypothesis testing
Research Projects and Working Groups
PASCAL 2
Projet GENOTO3D de l'ACI "Masses de Données"
GdT "Apprentissage et séquences" de l'AS CNRS ASAB
| Project | Last update |
| GENOTO3D | 11-24-06 |
| ASAB | 04-08-04 |
Software
Software of the multi-class SVM of Weston and Watkins (M-SVM)
Software of the M-SVM for protein sequence processing
Software of the M-SVM^2
| Application | Last update |
| Standard M-SVM | 06-30-08 |
| M-SVM for proteins | 04-04-08 |
| M-SVM^2 | 11-22-09 |
User's guide
Technical documentation
M-SVMs, theory and application for protein secondary structure prediction (slides)
Teaching
Multi-Class Support Vector Machines (pdf).
Summer School NN2008, Porto.
Multi-Class Support Vector Machines (ps).
Summer School NN2008, Porto.
Protein Secondary Structure Prediction with Multi-Class Support Vector Machines (pdf).
Summer School NN2008, Porto.
Protein Secondary Structure Prediction with Multi-Class Support Vector Machines (ps).
Summer School NN2008, Porto.
Some papers
Full publication list
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.
Estimation et contrôle des performances en généralisation des réseaux de neurones.
Y. Guermeur and 0. 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
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).
Journal of Machine Learning Research (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
Radius-Margin Bound on the Leave-One-Out Error of the LLW-M-SVM.
E. Monfrini and Y. Guermeur (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
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 segment 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.
Radius-Margin Bound on the Leave-One-Out Error of the LLW-M-SVM.
E. Monfrini and Y. Guermeur (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.
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.

Last modified 01-01-2010
e-mail: Yann.Guermeur@loria.fr