|
Alexandre Lacoste, Francois Laviolette, Mario Marchand. Bayesian Comparison
of Machine Learning Algorithms on Single and
Multiple Datasets, JMLR W&CP 22: 665-675,
2012 François Laviolette, Mario
Marchand, and Jean-Francis Roy. From PAC-Bayes Bounds to Quadratic Programs for Majority
Votes. In Proceedings of the
28th International Conference on Machine
Learning, Bellevue, WA, USA, June
2011. [ bib |
.pdf |
Extended
Version | Source
Code ] Pascal Germain, Alexandre
Lacoste, François Laviolette, Mario Marchand,
and Sara Shanian. A PAC-Bayes Sample Compression Approach
to Kernel Methods. In
Proceedings of the
28th International Conference on Machine
Learning, Bellevue, WA, USA, June
2011. [ bib |
.pdf |
Supplementary Material |
Source
Code ] Mohak Shah, Mario Marchand, Jacques Corbeil, Feature Selection with Conjunctions of Decision Stumps and
Learning from Microarray
Data. IEEE
Trans. Pattern Anal. Mach. Intell.
34(1): 174-186 (2012). For the preliminary version see also: http://arxiv.org/abs/1005.0530 . A. Lacasse,
F. Laviolette, M. Marchand, and F. Turgeon-Boutin.
Learning with Randomized Majority Votes. Springer LNCS
vol. 6322, pages 162-177, (2010). [ bib |
.pdf ] François Laviolette, Mario Marchand, Mohak Shah,
and Sara Shanian. Learning
the set covering machine by bound minimization
and margin-sparsity trade-off. Machine Learning, Vol. 78, pp.
175—201 (2010). Pascal
Germain, Alexandre Lacasse, Francois
Laviolette, Mario Marchand and Sara Shanian. From
PAC-Bayes Bounds to KL Regularization, Advances in Neural Information
Processing Systems 22, Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, A. Culotta editors, pp. 603—610 (2009). Pascal Germain, Alexandre Lacasse, François
Laviolette, and Mario Marchand. PAC-Bayesian Learning of Linear Classifiers. Proceedings
of the 26th International Conference on Machine Learning (ICML 2009),
(2009). Sébastien Boisvert, Mario Marchand, François
Laviolette, and Jacques Corbeil, HIV-1 coreceptor
usage prediction without multiple alignments: an application of string
kernels. Retrovirology, doi:10.1186/1742-4690-5-110, vol 5:11, 14 pages
(2008). François
Laviolette, Mario Marchand, and Sara Shanian. Selective
Sampling for Classification. Advances in Artificial
Intelligence, Springer LNAI vol. 5032, pp.
191--202 (2008). Sébastien Quirion, Chantale
Duchesne, Denis Laurendeau and Mario Marchand, Comparing
GPLVM Approaches for Dimensionality Reduction in Character Animation. Journal
of WSCG, vol. 16, no 1-3, pp. 41-48, 2008. Zakria Hussain,
François Laviolette and Mario Marchand, John Shawe-Taylor, Spencer
Charles Brubaker, Matthew D. Mullin. Revised Loss Bounds for the
Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced
Data, Journal of Machine Learning Research, vol. 8, pp. 2533--2549 (2007). François
Laviolette and Mario Marchand. PAC-Bayes Risk Bounds for
Stochastic Averages and Majority Votes of Sample-Compressed Classifiers, Journal of Machine Learning Research, vol. 8, pp. 1461--1487
(2007). Pascal
Germain, Alexandre Lacasse, François Laviolette, and Mario Marchand. PAC-Bayes Risk Bounds for General Loss Functions, Advances in Neural
Information Processing Systems 19 (Proceedings of NIPS 2006), B. Schölkopf, J. Platt, and T. Hoffman editors,
MIT-Press, pp. 449--456, Cambridge MA (2007). Alexandre
Lacasse, François Laviolette, Mario Marchand, Pascal Germain and Nicolas Usunier. PAC-Bayes Bounds for the Risk of the Majority
Vote and the Variance of the Gibbs Classifier, Advances in Neural
Information Processing Systems 19 (Proceedings of NIPS 2006), B. Schölkopf, J. Platt, and T. Hoffman editors,
MIT-Press, pp. 769--776, Cambridge MA (2007). François Laviolette, Mario Marchand and Mohak
Shah. A
PAC-Bayes Approach to the Set Covering Machine. Advances
in Neural Information Processing Systems 18 (Proceedings of
NIPS 2005). pp. 731--738, MIT-Press
(2006). François Laviolette, Mario Marchand, and Mohak
Shah. Margin-Sparsity Trade-off
for the Set Covering Machine. Proceedings of the 16th
European Conference on Machine Learning (ECML'05). Springer LNAI
vol. 3720, pp. 206--217 (2005). François Laviolette and Mario Marchand. PAC-Bayes Risk Bounds for Sample-Compressed
Gibbs Classifiers. Proceedings of the Twenty
Second International Conference on Machine Learning (ICML'05), pp.
481--488, ACM Press (2005). Mario Marchand and Marina Sokolova. Learning with Decision Lists
of Data-Dependent Features, Journal of Machine Learning Research, vol. 6, pp.
427-451 (2005). Mario Marchand and Mohak Shah. PAC-Bayes
Learning of Conjunctions and Classification of Gene-Expression Data, Advances in Neural Information Processing
Systems 17 (Proceedings of NIPS 2004), pp. 881-888, MIT-Press,
Cambridge, MA, (2005). Mario Marchand, Mohak Shah, John Shawe-Taylor,
and Marina Sokolova The Set Covering Machine with Data-Dependent
Half-Spaces, Proceedings of the Twentieth International Conference on
Machine Learning (ICML'2003), pp. 520--527, AAAI Press, Menlo Park
CA, (2003). Marina Sokolova, Mario Marchand, Nathalie Japkowicz, and John Shawe-Taylor, The
Decision List Machine, Advances in Neural Information Processing
Systems 15, pp. 921--928, MIT-Press, Cambridge, MA, USA,
(2003). Mario Marchand and John Shawe-Taylor, The Set Covering Machine, Journal of Machine
Learning Research, vol. 3, pp. 723-746 (2002). Mario Marchand and John Shawe-Taylor, Learning
with the Set Covering Machine, Proceedings of the Eighteenth International
Conference on Machine Learning (ICML'2001), pp. 345--352, Morgan
Kaufmann, San Francisco CA, (2001). Pal Rujan and Mario
Marchand, Computing
the Bayes Kernel Classifier, in A. J. Smola, P.
L. Bartlett, B. Schoelkopf, D. E. Schuurmans eds., Advances in Large Margin
Classifiers, pp. 329--347, MIT Press, Cambridge MA (2000). Mario Marchand and Saeed
Hadjifaradji, Strong Unimodality
and Exact Learning of mu-Perceptron Networks in D. S. Touretzky,
M. C. Mozer, M. E. Hasselmo,
eds., Advances in Neural Information Processing Systems 8, pp.
288--294, MIT Press, Cambridge MA, (1996). Mostefa Golea,
Mario Marchand and Thomas Hancock, On Learning mu-Perceptron Networks On the
Uniform Distribution, Neural Networks, vol. 9, pp. 67--82 (1996). Mario Marchand and Saeed
Hadjifaradji, Learning Stochastic Perceptrons under k-Blocking Distribution, in G. Tesauro,
D. S. Touretzky and T. K. Leen,
eds., Advances in Neural Information Processing Systems 7, pp.
279--286, MIT Press, Cambridge MA, (1995). Thomas Hancock, Mostefa
Golea and Mario Marchand. Learning Nonoverlapping
Perceptron Networks from Examples and Membership Queries, Machine Learning, vol. 16, pp. 161--183
(1994). Mostefa Golea
and Mario Marchand, On Learning Simple Deterministic and
Probabilistic Neural Concepts, in Shawe-Talor John , Anthony Martin, eds., Computational Learning
Theory: EuroCOLT'93, pp. 47--60, Oxford University Press, (1994). Mostefa Golea
and Mario Marchand, Average Case Analysis of the Clipped Hebb Rule for Nonoverlapping
Perceptron Networks, Proceedings of the Sixth Annual ACM Conference
on Computational Learning Theory, pp. 151--157, ACM press, (1993). Mario Marchand and Mostefa
Golea, An Approximation Algorithm to Find the Largest
Linearly Separable Subset of Training Examples, World Congress on Neural
Networks'93: Proceedings of the 1993 Annual Meeting of the International
Neural Network Society, vol. 3, pp. 556--559, Hillsdale, NJ: Erlbaum Associates, (1993). Mario Marchand and Mostefa
Golea, A Constructive Algorithm for Neural Decision
Lists, World Congress on Neural Networks'93: Proceedings of the 1993
Annual Meeting of the International Neural Network Society, vol. 3, pp. 560--563,
Hillsdale, NJ: Erlbaum Associates, (1993). Mostefa Golea,
Mario Marchand and Thomas Hancock, On Learning mu-Perceptron Networks with Binary
Weights, in Giles C.L., Hanson S.J. and Cowan J.D. (eds.), Advances in
Neural Information Processing Systems~5, pp. 591--598, San Mateo CA,
Morgan Kaufmann Publishers, (1993). Mostefa Golea
and Mario Marchand, Learning Curves of the Clipped
Hebb Rule for Networks with Binary Weights, Journal of Physics A,
vol. 26, pp 5751--5766 (1993). Mostefa Golea
and Mario Marchand, On Learning Perceptrons
with Binary Weights, Neural Computation, vol. 5, pp. 767--782 (1993). Mario Marchand and Mostefa
Golea. On Learning Simple Neural
Concepts: from Halfspace Intersections to Neural
Decision Lists, Network: Computation in Neural Systems, vol. 4, pp. 67--85 (1993) Mostefa Golea
and Mario Marchand, Polynomial Time Algorithms for Learning Neural Nets
of Nonoverlapping Perceptrons,
Computational Intelligence, vol. 9, pp. 155--170 (1993). Mostefa Golea
and Mario Marchand, A Growth Algorithm for Neural Network Decision
Trees, Europhysics Letters, vol. 12, pp. 205--210
(1990). Mario Marchand, Mostefa Golea and Pal Rujan, A Convergence Theorem for Sequential Learning in Two Layer Perceptrons, Europhysics
Letters, vol. 11, pp. 487--492 (1990). Pal Rujan and Mario Marchand. A Geometric Approach to
Learning in Neural Networks, Proceedings of
the International Joint Conference on Neural Networks II, IEEE TAB Neural
Network Committee, pp. 105--109 (1989). Pal Rujan and Mario Marchand. Learning by Minimizing
Resources in Neural Network, Complex
Systems, vol. 3, pp 229--242 (1989). |