Publications

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

There are 357 references in this bibliography folder.

Klinkenberg, R and Joachims, T (2000).
Detecting Concept Drift with Support Vector Machines
In: Proceedings of the Seventeenth International Conference on Machine Learning (ICML), San Francisco, Morgan Kaufmann.

Kowalczyk, A (2000).
Maximal Margin Perceptron
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 75-114, Cambridge, MA, MIT Press.

Laskov, P (2000).
An Improved Decomposition Algorithm for Regression Support Vector Machines
In: Advances in Neural Information Processing Systems 12, ed. by S.A. Solla and T.K. Leen and K.-R. Müller, pp. 484–490, MIT Press.

Lemm, JC, Uhlig, J, and Weiguny, A (2000).
A Bayesian Approach to Inverse Quantum Statistics
Phys. Rev. Lett., 84:2068–2071.

Lin, C (2000).
On the convergence of the decomposition methods for support vector machines
NTU.

Lin, Y, Lee, Y, and Wahba, G (2000).
Support Vector Machines for Classification in Nonstandard Situations
University of Wisconsin-Madison, Department of Statistics TR(1016).

Lin, Y, Wahba, G, Zhang, H, and Lee, Y (2000).
Statistical Properties and Adaptive Tuning of Support Vector Machines
University of Wisconsin-Madison, Department of statistics technical report(1022).

Mangasarian, O (2000).
Generalized 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. 135-146, Cambridge, MA, MIT Press.

Mangasarian, OL and Musicant, DR (2000).
Active Support Vector Machine Classification
Data Mining Institute, Computer Sciences Department, University of Wisconsin, Madison, Wisconsin.

Mangasarian, OL and Musicant, DR (2000).
Lagrangian Support Vector Machines
Data Mining Institute, Computer Sciences Department, University of Wisconsin, Madison, Wisconsin.

Mason, L, Baxter, J, Bartlett, P, and Frean, M (2000).
Functional Gradient Techniques for Combining Hypotheses
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 221-246, Cambridge, MA, MIT Press.

Mitra, P, Murthy, CA, and Pal, SK (2000).
Data Condensation in Large Databases by Incremental Learning with Support Vector Machines
In: Proc. Intl. Conf. on Pattern Recognition (ICPR2000).

Oliver, N, Sch\"olkopf, B, and Smola, AJ (2000).
Natural Regularization from Generative Models
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 51-60, Cambridge, MA, MIT Press.

Opper, M and Winther, O (2000).
Gaussian Processes and SVM: Mean Field and Leave-One-Out
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 311-326, Cambridge, MA, MIT Press.

Pavlov, D, Chudova, D, and Smyth, P (2000).
Towards Scalable Support Vector Machines Using Squashing
In: Proceedings of the International Conference on Knowledge Discovery in Databases, KDD-2000.

Pavlov, D, Mao, J, and Dom, B (2000).
Scaling-up Support Vector Machines Using Boosting Algorithm
In: Proceedings of the International Conference on Pattern Recognition, ICPR-2000.

Platt, J (2000).
Probabilities for SV Machines
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 61-74, Cambridge, MA, MIT Press.

R., R, L.J., T, and A., C (2000).
Kernel Principal Component Regression with EM Approach to Nonlinear Principal Components Extraction.
RIKEN.

R\"atsch, G, Sch\"olkopf, B, Smola, AJ, Mika, S, Onoda, T, and M\"uller, K (2000).
Robust Ensemble Learning
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 207-220, Cambridge, MA, MIT Press.

Risau-Gusman, S and Gordon, MB (2000).
Generalization properties of finite-size polynomial support vector machines
Physical Review E, 62(5):7092-7099.

Rosipal, R and Girolami, M (2000).
An expectation maximization approach to nonlinear component analysis
Neural Computation.

Ruj\'an, P and Marchand, M (2000).
Computing the Bayes Kernel Classifier
In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 329-348, Cambridge, MA, MIT Press.

Sch\"olkopf, B (2000).
A Short Tutorial on Kernels
Miscellaneous publication.

Sch\"olkopf, B, Herbrich, R, Smola, A, and Williamson, R (2000).
A Generalized Representer Theorem
NeuroCOLT.

Sch\"olkopf, B, Platt, J, and Smola, A (2000).
Kernel method for percentile feature extraction
Microsoft Research, TR MSR(2000-22), Redmond, WA.