Team MODBIO

Members
Overall Objectives
Scientific Foundations
Application Domains
Software
New Results
Other Grants and Activities
Dissemination
Bibliography

Bibliography

Major publications by the team in recent years

[1]
E. Althaus, A. Caprara, H.-P. Lenhof, K. Reinert.
Multiple sequence alignment with arbitrary gap costs: Computing an optimal solution using polyhedral combinatorics., in: Proc. European Conference on Computational Biology, Bioinformatics, October 2002, vol. 18, no Supplement 2, p. S4–S16.
[2]
E. Althaus, K. Mehlhorn.
Traveling Salesman-Based Curve Reconstruction in Polynomial Time, in: SIAM Journal on Computing, 2001, vol. 31, no 1, p. 27–66.
[3]
E. Balas, A. Bockmayr, N. Pisaruk, L. Wolsey.
On unions and dominants of polytopes, in: Mathematical Programming, Ser. A, 2004, vol. 299, p. 223-239.
[4]
A. Bockmayr, A. Courtois.
Using hybrid concurrent constraint programming to model dynamic biological systems, in: 18th International Conference on Logic Programming, ICLP'02, Copenhagen, Springer, LNCS 2401, 2002, p. 85-99.
[5]
A. Bockmayr, J. N. Hooker.
Constraint Programming, in: 12: Discrete Optimization, K. Aardal, G. Nemhauser, R. Weismantel (editors), Handbooks in Operations Research and Management Science, Elsevier, 2005, chap. 10, p. 559–600.
[6]
A. Bockmayr, V. Weispfenning.
Solving numerical constraints, in: Handbook of Automated Reasoning, A. Robinson, A. Voronkov (editors), Elsevier, 2001, vol. 1, chap. 12, p. 751-842.
[7]
Y. Guermeur, A. Elisseeff, D. Zelus.
Bound on the risk for M-SVMs, in: Statistical Learning, Theory and Applications, 2002, p. 48–52.
[8]
Y. Guermeur, C. Geourjon, P. Gallinari, G. Deléage.
Improved performance in protein secondary structure prediction by inhomogeneous score combination, in: Bioinformatics, 1999, vol. 15, no 5, p. 413–421.
[9]
Y. Guermeur.
Combining discriminant models with new multi-class SVMs, in: Pattern Analysis and Applications, 2002, vol. 5, no 2, p. 168–179.
[10]
Y. Guermeur, A. Lifchitz, R. Vert.
A kernel for protein secondary structure prediction, in: Kernel Methods in Computational Biology, B. Schölkopf, K. Tsuda, J.-P. Vert (editors), The MIT Press, 2004, p. 193–206.
[11]
Y. Guermeur, H. Paugam-Moisy.
Théorie de l'apprentissage de Vapnik et SVM, Support Vector Machines, in: Apprentissage Automatique, M. Sebban, G. Venturini (editors), Hermès, 1999, p. 109–138.
[12]
V. Y. Lunin, A. Urzhumtsev, A. Bockmayr.
Direct phasing by binary integer programming, in: Acta Crystallographica Section A, 2002, vol. 58, p. 283-291.
[13]
P. Musé, F. Sur, F. Cao, Y. Gousseau, J.-M. Morel.
An a contrario decision method for shape element recognition, Technical report, CMLA, ENS Cachan, 2004, no 2004-16.

Publications of the year

Articles in refereed journals and book chapters

[14]
E. Althaus, A. Caprara, H.-P. Lenhof, K. Reinert.
Aligning Multiple sequences by Cutting Planes, in: Mathematical Programming, to appear.
[15]
E. Althaus, S. Funke, S. Har-Peled, J. Könemann, E. A. Ramos, M. Skutella.
Approximating k-hop minimum-spanning trees, in: Operations Research Letters, March 2005, vol. 33, no 2, p. 115–120.
[16]
F. Denis, R. Gilleron, F. Letouzey.
Learning From Positive and Unlabeled Examples, in: Theoretical Computer Science, 2005, vol. 348, p. 70-83.
[17]
P. Dupont, F. Denis, Y. Esposito.
Links between Probabilistic Automata and Hidden Markov Models: probability distributions, learning models and induction algorithms, in: Pattern Recognition: Special Issue on Grammatical Inference Techniques & Applications, 2005, vol. 38/9, p. 1349-1371.
[18]
Y. Guermeur, A. Elisseeff, D. Zelus.
A comparative study of multi-class support vector machines in the unifying framework of large margin classifiers, in: Applied Stochastic Models in Business and Industry, 2005, vol. 21, no 2, p. 199–214.
[19]
Y. Guermeur, O. Teytaud.
Estimation et contrôle des performances en généralisation des réseaux de neurones, in: Apprentissage Connexionniste, Y. Bennani (editor), to appear, Hermès, 2005.
[20]
P. Musé, F. Sur, F. Cao, Y. Gousseau, J.-M. Morel.
An a contrario decision method for shape element recognition, in: International Journal of Computer Vision, to appear, 2006.
[21]
P. Musé, F. Sur, F. Cao, Y. Gousseau, J.-M. Morel.
Shape recognition based on an a contrario methodology, in: Statistics and analysis of shapes, H. Krim, A. Yezzi (editors), Birkhauser, 2006.

Publications in Conferences and Workshops

[22]
E. Althaus, R. Naujoks.
Computing Steiner Minimum Trees in Hamming Metric, in: Proceedings of the 17th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA-06), Miami, USA, to appear, 2006.
[23]
C. Capponi, G. Fichant, Y. Quentin, F. Denis.
Boosting Blast, in: Applied Stochastic Models and Data Analysis ASMDA 2005, Brest, France, 2005.
[24]
C. Capponi, G. Fichant, Y. Quentin, F. Denis.
Boosting BLAST for classifying protein domains, in: Actes de JOBIM 2005, Lyon, France, 2005.
[25]
F. Denis, Y. Esposito.
Learning classes of Probabilistic Automata, in: COLT 2004, LNAI, 2004, no 3120, p. 124-139.
[26]
F. Denis, Y. Esposito.
Rational stochastic languages, in: TAGI 2005, 2005.
[27]
Y. Guermeur, M. Maumy, F. Sur.
Model selection for multi-class SVMs, in: ASMDA'05, Brest, France, 2005, p. 507–516.
[28]
C. Magnan.
Apprentissage semi-supervisé asymétrique et estimations d'affinités locales dans les protéines, in: Actes de CAP 05, F. Denis (editor), PUG, 2005, p. 297-312.
[29]
N. Sapay, Y. Guermeur, G. Deléage.
Prediction of in-plane amphipathic membrane segments based on an SVM method, in: JOBIM'05, Lyon, France, 2005, p. 299–311.

Internal Reports

[30]
F. Cao, J. Delon, A. Desolneux, P. Musé, F. Sur.
A unified framework for detecting groups and application to shape recognition, (submitted to a journal), INRIA, 2005, no RR-5695
http://www.inria.fr/rrrt/rr-5695.html.
[31]
Y. Darcy, Y. Guermeur.
Radius-margin bound on the leave-one-out error of multi-class SVMs, Technical report, INRIA, 2005, no RR-5780
http://www.inria.fr/rrrt/rr-5780.html.

References in notes

[32]
C. Burges.
A tutorial on support vector machines for pattern recognition, in: Data Mining and Knowledge Discovery, June 1998, vol. 2, no 2, p. 121–167.
[33]
B. Carl, I. Stephani.
Entropy, compactness, and the approximation of operators, Cambridge University Press, Cambridge, UK, 1990.
[34]
W. J. Cook, W. H. Cunningham, W. R. Pulleyblank, A. Schrijver.
Combinatorial Optimization, Wiley, 1998.
[35]
C. Cortes, V. Vapnik.
Support-Vector Networks, in: Machine Learning, 1995, vol. 20, p. 273–297.
[36]
V. Gupta, R. Jagadeesan, V. Saraswat.
Computing with Continuous Change, in: Science of computer programming, 1998, vol. 30, no 1-2, p. 3-49.
[37]
V. A. Saraswat.
Concurrent constraint programming, ACM Doctoral Dissertation Awards, MIT Press, 1993.
[38]
P. van Hentenryck, V. Saraswat.
Strategic directions in constraint programming, in: ACM Computing Surveys, 1996, vol. 28, no 4, p. 701–726.
[39]
V. Vapnik.
Estimation of Dependences Based on Empirical Data., Springer-Verlag, N.Y., 1982.
[40]
V. Vapnik.
Statistical learning theory, John Wiley & Sons, Inc., N.Y., 1998.

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