Publications

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

There are 357 references in this bibliography folder.

Sch\"olkopf, B, Williamson, R, Smola, A, Shawe-Taylor, J, and Platt, J (2000).
Support vector method for novelty detection
In: Neural Information Processing Systems.

Schölkopf, B (2000).
Statistical Learning and Kernel Methods
Microsoft Research, MSR-TR(2000-23).

Schölkopf, B (2000).
The Kernel Trick for Distances
Microsoft Research, TR MSR(2000-51), Redmond, WA.

Shawe-Taylor, J and Cristianini, N (2000).
Margin Distribution and Soft Margin
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 349-358, Cambridge, MA, MIT Press.

Shawe-Taylor, J and Karakoulas, G (2000).
Towards a Strategy for Boosting Regressors
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 247-258, Cambridge, MA, MIT Press.

Smola, A and Sch\"olkopf, B (2000).
Sparse Greedy Matrix Approximation for Machine Learning
In: International Conference on Machine Learning.

Smola, A, Elisseeff, A, Sch\"olkopf, B, and Williamson, R (2000).
Entropy Numbers for Convex Combinations and MLPs
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 369-388, Cambridge, MA, MIT Press.

Smola, A, Shawe-Taylor, J, Sch\"olkopf, B, and Williamson, R (2000).
The Entropy Regularization Information Criterion
In: Advances in Neural Information Processing Systems.

Suykens, J and Vandewalle, J (2000).
Recurrent least squares support vector machines
IEEE Transactions on Circuits and Systems-I, 47(7):1109-1114.

Suykens, JA, Lukas, L, and Vandewalle, J (2000).
Sparse approximation using least squares support vector machines.
In: IEEE International Symposium on Circuits and Systems ISCAS'2000.

Suykens, JA, Lukas, L, and Vandewalle, J (2000).
Sparse Least Squares Support Vector Machine Classifiers
In: ESANN'2000 European Symposium on Artificial Neural Networks, pp. 37-42.

Tipping, M (2000).
The Relevance Vector Machine
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.

Tong, S and Koller, D (2000).
Support Vector Machine Active Learning with Applications to Text Classification
In: Proceedings of the Seventeenth International Conference on Machine Learning.

Tong, S and Koller, D (2000).
Restricted Bayes Optimal Classifiers
In: Proceedings of AAAI.

Tresp, V (2000).
A Bayesian Committee Machine
Neural Computation, 12(11):2719-2741.

Tresp, V (2000).
The Generalized Bayesian Committee Machine
In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2000, pp. 130-139.

Vapnik, V and Chapelle, O (2000).
Bounds on error expectation for SVM
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 261-280, Cambridge, MA, MIT Press.

Wahba, G (2000).
An introduction to model building with reproducing kernel Hilbert spaces
Statistics Department University of Wisconsin-Madison, TR(1020).

Wahba, G (2000).
An Introduction to Model Building with Reproducing Kernel Hilbert Spaces
University of Wisconsin-Madison, Statistics Dept..

Wahba, G, Lin, Y, and Zhang, H (2000).
GACV for 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. 297-311, Cambridge, MA, MIT Press.

Watkins, C (2000).
Dynamic Alignment Kernels
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 39-50, Cambridge, MA, MIT Press.

Weston, J and Herbrich, R (2000).
Adaptive Margin 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. 281-296, Cambridge, MA, MIT Press.

Williams, CK and Seeger, M (2000).
The Effect of the Input Density Distribution on Kernel-based Classifiers
In: Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), ed. by Langley, P., Morgan Kaufmann.

Williams, CK and Vivarelli, F (2000).
Upper and lower bounds on the learning curve for Gaussian processes
Machine Learning, 40(1):77-102.

Yang, M and Ahuja, N (2000).
A Geometric Approach to Train Support Vector Machines
In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 430-437.