Publications

This folder holds the following references to publications, sorted by year and author.

There are 357 references in this bibliography folder.

Twining, c and Taylor, C (2001).
Kernel principal Component Analysis and the construction of non-linear Active Shape Models
In: British Machine Vision Conference, vol. 1, pp. 23-32.

Wahba, G, Lin, Y, Lee, Y, and Zhang, H (2001).
On the Relation Between the GACV and Joachims' \xi\alpha Method for Tuning Support Vector Machines, With Extensions to the Non-Standard Case
Department of Statistics, University of Wisconsin, Madison WI.

Wahba, G, Lin, Y, Lee, Y, and Zhang, H (2001).
Optimal properties and adaptive tuning of standard and nonstandard Support Vector Machines
Statistics Department University of Wisconsin, Madison WI.

Xu, J, Zhang, X, and Li, Y (2001).
Large Margin Kernel Pocket Algorithm
Proceedings of IJCNN'01, 2:1480-1485.

Xu, J, Zhang, X, and Li, Y (2001).
Kernel MSE Algorithm: A Unified Framework for KFD, LS-SVM
Proceedings of IJCNN'01, 2:1486-1491.

Baudat, G and Anouar, F (2000).
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation, 12(1).

Bennett, K, Demiriz, A, and Shawe–Taylor, J (2000).
A Column Generation Algorithm for Boosting
In: Proceedings of Seventeenth International Conference on Machine Learning (ICML’2000), ed. by Pat Langley, pp. 65-72, Morgan Kaufmann.

C.Campbell (2000).
Algorithmic Approaches to Training Support Vector Machnies: A Survey
In: Proceedings of ESANN2000, pp. 8.

Campbell, C (2000).
An Introduction to Kernel Methods.
In: Radial Basis Function Networks: Design and Applications, ed. by R.J. Howlett and L.C. Jain, pp. 31, Springer Verlag, Berlin.

Campbell, C, Cristianini, N, and Smola, A (2000).
Query Learning with Large Margin Classifiers
Proceedings of ICML2000 (Stanford, CA, 2000).:8.

Chapelle, O and Vapnik, V (2000).
Model Selection for Support Vector Machines
In: Advances in Neural Information Processing Systems 12, ed. by Sara A. Solla and Todd K. Leen and Klaus-Robert M\"uller, Cambridge, Mass: MIT Press.

Chew, H, Bogner, R, and Lim, C (2000).
Target Detection in Radar Imagery using Support Vector Machines with Training Size Biasing
In: International Conference on Control, Automation, Robotics and Vision, ICARCV 2000, Singapore, pp. CD-ROM.

Collobert, R and Bengio, S (2000).
Support Vector Machines for Large-Scale Regression Problems
IDIAP.

Cristianini, N and Shawe-Taylor, J (2000).
An Introduction to Support Vector Machines
Cambridge University Press, Cambridge, UK.

DeCoste, D and Wagstaff, K (2000).
Alpha Seeding for Support Vector Machines
In: International Conference onInternational Conference on Knowledge Discovery and Data Mining (KDD-2000).

Dietrich, R and Opper, M (2000).
Support Vectors and Statistical Mechanics
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 359-368, Cambridge, MA, MIT Press.

Evgeniou, T, Perez-Breva, L, Pontil, M, and Poggio, T (2000).
Bounds on the Generalization Performance of Kernel Machines Ensembles
In: International Conference on Machine Learning.

Evgeniou, T, Pontil, M, and Poggio, T (2000).
Regularization Networks and Support Vector Machines
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 171-204, Cambridge, MA, MIT Press.

Evgeniou, T, Pontil, M, and Poggio, T (2000).
Regularization Networks and Support Vector Machines
Advances in Computational Mathematics.

Evgeniou, T, Pontil, M, Papageorgiou, C, and Poggio., T (2000).
Image representations for object detection using kernel classifiers
In: ACCV.

Guyon, I and Stork, D (2000).
Linear Discriminant and Support Vector Classifiers
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 147-169, Cambridge, MA, MIT Press.

Guyon, I and Stork, D (2000).
Linear Discriminant and Support Vector Classiers
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 147-169, Cambridge, MA, MIT Press.

Herbrich, R, Graepel, T, and Obermayer, K (2000).
Large Margin Rank Boundaries for Ordinal Regression
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 115-132, Cambridge, MA, MIT Press.

Joachims, T (2000).
Estimating the Generalization Performance of a SVM Efficiently
In: Proceedings of the International Conference on Machine Learning, San Francisco, Morgan Kaufman.

Kecman, V and Hadzic, I (2000).
Support Vectors Selection by Linear Programming
In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2000), Como, Italy, vol. 5, pp. 193-198.