publications


F. Lauer
Error bounds for piecewise smooth and switching regression
Submitted, 2017
[abstract]

Journal papers

2018

F. Lauer
Global optimization for low-dimensional switching linear regression and bounded-error estimation
Automatica, to appear, 2018
[abstract]

2017

F. Lauer
MLweb: A toolkit for machine learning on the web
Neurocomputing, to appear, 2017
[website] [code] [abstract]

T.S. Illés, M. Burkus, S. Somoskeôy, F. Lauer, F. Lavaste, and J.F. Dubousset.
The horizontal plane appearances of scoliosis: what information can be obtained from top-view images?
International Orthopaedics, 41(11):2303-2311, 2017
[abstract]

2016

F. Lauer
On the complexity of switching linear regression
Automatica, 74:80-83, 2016
[abstract]

2015

F. Lauer
On the complexity of piecewise affine system identification
Automatica, 62:148-153, 2015
[abstract]

F. Lauer and H. Ohlsson
Finding sparse solutions of polynomial systems of equations via group sparsity optimization
Journal of Global Optimization, 62(2):319-349, 2015
[code] [abstract]

2014

V.L. Le, F. Lauer, and G. Bloch
Selective l1 minimization for sparse recovery
IEEE Transactions on Automatic Control, 59(11):3008-3013, 2014
[abstract]

T. Pham Dinh, H. M. Le, H. A. Le Thi, and F. Lauer
A difference of convex functions algorithm for switched linear regression
IEEE Transactions on Automatic Control, 59(8):2277-2282, 2014
[abstract]

2013

F. Lauer,
Estimating the probability of success of a simple algorithm for switched linear regression
Nonlinear Analysis: Hybrid Systems, 8:31-47, 2013
[code] [online demo] [abstract]

2011

V.L. Le, G. Bloch, and F. Lauer
Reduced-size kernel models for nonlinear hybrid system identification
IEEE Transactions on Neural Networks, 22(12):2398-2405, 2011
[abstract]

F. Lauer and Y. Guermeur,
MSVMpack: a Multi-Class Support Vector Machine package
Journal of Machine Learning Research, 12:2269-2272, 2011
[code] [abstract]

F. Lauer, G. Bloch, and R. Vidal,
A continuous optimization framework for hybrid system identification
Automatica 47(3):608-613, 2011
[code] [abstract]

2008

F. Lauer and G. Bloch,
Incorporating prior knowledge in Support Vector Regression
Machine Learning 70(1):89-118, 2008
[abstract]

F. Lauer and G. Bloch,
Incorporating prior knowledge in Support Vector Machines for classification: a review
Neurocomputing 71(7-9):1578-1594, 2008
[abstract]

G. Bloch, F. Lauer, G. Colin, and Y. Chamaillard,
Support Vector Regression from simulation data and few experimental samples
Information Sciences 178(20):3813-3827, 2008
[abstract]

2007

F. Lauer, C. Y. Suen, and G. Bloch,
A trainable feature extractor for handwritten digit recognition
Pattern Recognition 40(6):1816-1824, 2007
[abstract]

2006

F. Lauer and G. Bloch,
Ho-Kashyap classifier with early stopping for regularization
Pattern Recognition Letters 27(9):1037:1044, 2006
[abstract]

Technical reports

F. Lauer and H. Ohlsson
Sparse phase retrieval via group-sparse optimization
arXiv preprint, arXiv:1402.5803, 2014
[abstract]

Book chapters

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

G. Bloch, F. Lauer and G. Colin,
On learning machines in engine control
In: Computational Intelligence in Automotive Applications, D. Prokhorov (Ed.), vol. 132 of Studies in Computational Intelligence, Springer, 2008

Conference papers

2017

K. Musayeva, F. Lauer and Y. Guermeur,
Metric entropy and Rademacher complexity of margin multi-category classifiers
In Proc. of the 26th Int. Conf. on Artificial Neural Networks (ICANN), 2017

2015

E. Didiot and F. Lauer,
Efficient optimization of multi-class support vector machines with MSVMpack
In Modelling, Computation and Optimization in Information Systems and Management Sciences, Proc. of MCO 2015, pp. 23-34, 2015

2014

F. Lauer and G. Bloch,
Piecewise smooth system identification in reproducing kernel Hilbert space
In Proc. of the 53rd IEEE Conf. on Decision and Control (CDC), Los Angeles, CA, USA, pp. 6498-6503, 2014

2013

V.L. Le, F. Lauer, L. Bako, and G. Bloch,
Learning nonlinear hybrid systems: from sparse optimization to support vector regression
In Proc. of the 16th ACM Int. Conf. on Hybrid Systems: Computation and Control (HSCC), Philadelphia, PA, USA, pp. 33-42, 2013

V.L. Le, F. Lauer, and G. Bloch,
Identification of linear hybrid systems: a geometric approach
In Proc. of the American Control Conference (ACC), Washington, DC, USA, pp. 830-835, 2013

L. Bako, V.L. Le, F. Lauer, and G. Bloch,
Identification of MIMO switched state-space models
In Proc. of the American Control Conference (ACC), Washington, DC, USA, pp. 71-76, 2013

2012

F. Thomarat, F. Lauer, and Y. Guermeur,
Cascading discriminant and generative models for protein secondary structure prediction
In Proc. of the 7th IAPR Int. Conf. on Pattern Recognition in Bioinformatics (PRIB), Tokyo, Japan, vol. 7632 of LNCS (LNBI), pp. 166-177, 2012

F. Lauer, V.L. Le, and G. Bloch,
Learning smooth models of nonsmooth functions via convex optimization
In Proc. of the 22nd IEEE Int. Workshop on Machine Learning for Signal Processing (MLSP), Santander, Spain, 2012

2010

F. Lauer, G. Bloch, and R. Vidal,
Nonlinear hybrid system identification with kernel models
In Proc. of the 49th IEEE Int. Conf. on Decision and Control (CDC), Atlanta, GA, USA, pp. 696-701, 2010

2009

F. Lauer and C. Schnörr,
Spectral clustering of linear subspaces for motion segmentation
In Proc. of the 12th IEEE Int. Conf. on Computer Vision (ICCV), Kyoto, Japan, pp. 678-685, 2009

F. Lauer, R. Vidal, and G. Bloch,
A product-of-errors framework for linear hybrid system identification
In Proc. of the 15th IFAC Symp. on System Identification (SYSID), Saint-Malo, France, pp. 563-568, 2009

2008

F. Lauer and G. Bloch,
Switched and piecewise nonlinear hybrid system identification
In Proc. of the 11th Int. Conf. on Hybrid Systems: Computation and Control (HSCC), St-Louis, MO, USA, vol. 4981 of LNCS, pp. 330-343, 2008

F. Lauer and G. Bloch,
A new hybrid system identification algorithm with automatic tuning
In Proc. of the 17th IFAC World Congress, Seoul, Korea, pp. 10207-10212, 2008

2007

G. Bloch, F. Lauer, G. Colin, and Y. Chamaillard,
Combining experimental data and physical simulation models in support vector learning
In Proc. of the 10th Int. Conf. on Eng. Appl. of Neural Networks (EANN), Thessaloniki, Greece, vol. 284 of CEUR-WS.org, pp. 284-295, 2007

2006

F. Lauer and G. Bloch,
Méthodes SVM pour l'identification
In Proc. of the Journées pour l'Identification et la Modélisation Expérimentale (JIME), Poitiers, France, 2006 (french)

2004

F. Lauer, M. Bentoumi, G. Bloch, G. Millérioux, and P. Aknin,
Ho-Kashyap with early stopping vs. soft-margin SVM for linear classifiers: an application
In Advances in Neural Networks - ISNN 2004, Dalian, China, vol. 3173 of LNCS, pp. 524-530, 2004

Talks

F. Lauer and G. Bloch,
A support vector machine approach to hybrid system identification (in french)
GT Identification du GDR-MACS, Paris, France, March 2008, et GT Systèmes Dynamiques Hybrides (SDH) du GDR-MACS, Paris, France, April 2008

F. Lauer,
Support Vector Machines for handwritten digit recognition
Japan-France Seminar on Analytical and Numerical Methods for Scientific Computing in Science and Engineering, Nancy, France, Feb. 2006

Thesis

F. Lauer, From support vector machines to hybrid system identification
PhD thesis, Université Henri Poincaré Nancy 1, France, 2008

F. Lauer,
Increasing the recognition rate of handwritten digit classifiers
Rapport de DEA, Université Henri Poincaré - Nancy 1, France, 2005, also published as a Technical Report, CENPARMI, Concordia University, Canada, 2005