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Recent Publications by BIOwulf Team Members
A. Ben-Hur, D. Horn, H.T. Siegelmann and V. Vapnik.
A support vector method for clustering.
Advances in Neural Information Processing Systems 13, Cambridge,
MA 2001. MIT Press.
A. Ben-Hur, H.T. Siegelmann and S. Fishman.
A theory of complexity for continuous time dynamics.
Journal of Complexity, accepted.
A. Ben-Hur and H.T. Siegelmann.
Computation in gene networks.
Lecture Notes in Computer Science, Springer.
Bienenstock, E., and Doursat, R.
A shape-recognition model using dynamical links.
Network 5(2), 241-258 1994.
O. Bousquet and F. Yvon.
Apprentissage Automatique et Simplicité, Mémoire d'option
de l'ENST 1999.
O. Bousquet, K. Balakrishnan and V. Honavar .
Is the Hippocampus a Kalman Filter?.
Proceedings of the Pacific Symposium on Biocomputing 3:619-630
1998.
O. Bousquet and A.Elisseeff.
Algorithmic Stability and Generalization Performance.
Advances in Neural Information Processing Systems 13, 2000, MIT
Press.
O. Chapelle, J. Weston, L. Bottou and V. Vapnik.
Vicinal Risk Minimization.
Submitted to NIPS*00.
O. Chapelle. Support Vector Machines for
Image Classification.
BS thesis, Ecole Normale Supérieure de Lyon, 1999
O. Chapelle, P. Haffner, and V. Vapnik.
SVMs for Histogram Based Image Classification.
IEEE Transaction on Neural Networks, 9, 1999.
O. Chapelle and V. Vapnik.
Model Selection for Support Vector Machines.
Advances in Neural Information Processing Systems, volume 12,
1999.
O. Chapelle, V. Vapnik, and J. Weston.
Transductive Inference for Estimating Values of Functions.
Advances in Neural Information Processing Systems, volume 12, 1999
O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee.
Choosing Kernel Parameters for Support Vector Machines .
Submitted to Machine Learning, 2000.
O. Chapelle, J. Weston, L. Bottou, and V. Vapnik.
Vicinal Risk Minimization.
Advances in Neural Information Processing Systems, volume 13,
2000.
O. Chapelle, V. Vapnik, and Y. Bengio.
Model Selection for Small Sample Regression.
Submitted to Machine Learning, 2000
Doursat, R., and Petitot, J.
Modèles dynamiques et linguistique cognitive : vers une sémantique
morphologique active.
Technical report Nr. 9809, in Rapports et documents du CREA, Paris 1998.
A. Elisseeff and H. Paugam-Moisy.
Jacobian Neural Network Learning Algorithms.
International Conference on Artificial Neural Networks, 1998,
573-578.
A. Elisseeff and H. Paugam-Moisy.
Size of multilayer networks for exact learning: analytic approach.
Advances in Neural Information Processing Systems 9, 162-168,
1996, MIT Press.
A. Elisseeff and H.
Paugam-Moisy.
a Randomized Algorithm for Learning Multilayer Networks in Polynomial
Time.
Neurocomputing, 29:3-24, 1999.
G. Gavin and A. Elisseeff.
Confidence bounds for the generalization performances of linear
combination of functions.
International Conference on Artificial Neural Networks, 1999,
IE.
Geman, S., Bienenstock, E., and Doursat, R.
Neural networks and the bias/variance dilemma.
Neural Computation 4, 1-58 2000.
Y. Guermeur, A. Elisseeff and H. Paugam-Moisy.
A new multi-class SVM based on a uniform convergence result.
Internation Joint Conference on Neural Network, 2000.
Y. Guermeur, A. Elisseeff and H. Paugam-Moisy.
Estimating the sample complexity of a multi-class discriminant
model.
International Conference on Artificial Neural Networks,
1999, p.310--315, IE.
I. Guyon, J. Weston, S. Barnhill and V. Vapnik.
Gene Selection for Cancer Classification using Support Vector Machines.
Submitted to Machine Learning.
I. Guyon and D. Stork.
Linear discriminant and support vector classifiers.
In Smola et al Eds. Advances in Large Margin Classiers. 147--169,
MIT Press, 2000.
I. Guyon, J. Makhoul, R. Schwartz, and V. Vapnik.
What size test set gives good error rate estimates?
PAMI, 20 (1):52--64, IEEE. 1998.
S. Mika, G. Rdtsch, B. Scholkopf, A. Smola, J. Weston,
K.-R. Muller.
Invariant Feature Extraction and Classification in Kernel Spaces.
To appear in the Proceedings of NIPS*99.
H. Paugam-Moisy, A. Elisseeff and Y. Guermeur.
Generalization performance of multi-class discriminant models.
Internation Joint Conference on Neural Networks, 2000.
B. Schölkopf and C. Burges and V. Vapnik.
Extracting support data for a given task.
Proceedings, First International Conference on Knowledge Discovery
& Data Mining.
U.M. Fayyad and R. Uthurusamy Eds, AAAI Press 1995.
B. Schölkopf, K. Sung, C. Burges, F. Girosi, P. Niyogi,
T. Poggio and V. Vapnik.
Comparing support vector machines with Gaussian kernels to radial basis
function classifiers.
IEEE Transactions in Signal Processing, 45:11:2758--2765, 1997
B. Schölkopf.
Support Vector Learning.
R. Oldenbourg Verlag, 1997.
B. Schölkopf, A. Smola and K. R. Müller.
Nonlinear component analysis as a kernel Eigenvalue problem.
Neural Computation, 10:5:1299--1319,1998.
B. Schölkopf, C.J. C. Burges and A. J. Smola.
Advances in Kernel Methods --- Support Vector Learning.
MIT Press, 1999
B. Schölkopf, A. Smola,
R.C. Williamson and P.L. Bartlett.
New Support Vector Algorithms.
Neural Computation, 12:5:1207--1245, 2000.
B. Schölkopf, J. Platt,
J. Shawe-Taylor, A. J. Smola and R. C. Williamson.
Estimating the support of a high-dimensional distribution.
Neural Computation, 13:7, 2001
B. Schölkopf and A.J. Smola.
Learning with Kernels.
MIT Press, 2001, (forthcoming)
A. Smola, A. Elisseeff, B. Schölkopf and
R.C. Williamson.
Entropy Numbers for Convex Combinations and Multilayer Networks.
Advances in Large Margin Classifiers, 1999.
Smola and Bartlett and Schölkopf and Schuurmans, MIT Press.
V. Vapnik and O. Chapelle.
Bounds on Error Expectation for Support Vector Machines. In
Neural Computation, 2000, 12:9.
Also in Advances in Large Margin Classifiers. MIT Press. 1999.
J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T.
Poggio, and V. Vapnik.
Feature Selection for SVMs.
Advances in Neural Information Processing Systems, volume 13, 2000.
A. Zien, G. Rätsch, S. Mika,
B. Schölkopf, T. Lengauer and K.R. Müller.
Engineering Support Vector Machine Kernels That Recognize Translation
Initiation Sites.
Bioinformatics, 2000, 16:9:799-807.
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