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by admin last modified 2008-11-11 09:39
Publications in the database on Kernel-Machines.Org

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

There are 357 references in this bibliography folder.

Sonnenburg, S, Rätsch, G, Jagota, A, and Müller, K (2002).
New Methods for Splice Site Recognition
In: Proceedings of the International Conference on Artifical Neural Networks.

Steinwart, I (2002).
On the optimal parameter choice for $\nu$-support vector machines
University Jena.

Suykens, J, Brabanter, JD, Lukas, L, and Vandewalle, J (2002).
Weighted least squares support vector machines: robustness and sparse approximation
Neurocomputing, 48(1–4):85–105.

T., VG, J.A.K., S, B., B, , VS, J., V, G., D, B., DM, and J., V (2002).
Benchmarking least squares support vector machine classifiers
Machine Learning.

Tsuda, K, Kawanabe, M, and Rätsch, G (2002).
A New Discriminative Kernel from Probabilistic Models
In: Advances in Neural information processings systems, ed. by T.G. Dietterich and S. Becker and Z. Ghahramani, vol. 14.

Wahba, G (2002).
Soft and Hard Classification by Reproducing Kernel Hilbert Space Methods
Proc.National Academy of Sciences, 99:16524-16530.

Warmuth, M, Rätsch, G, Mathieson, M, Liao, J, and Lemmen, C (2002).
Active Learning in the Drug Discovery Process
In: Advances in Neural information processings systems, ed. by T.G. Dietterich and S. Becker and Z. Ghahramani, vol. 14.

Zhang, H, Wahba, G, Lin, Y, Voelker, M, Ferris, M, Klein, R, and Klein, B (2002).
Variable Selection and Model Building via Likelihood Basis Pursuit
Statistics Department University of Wisconsin, Madison WI.

Campbell, C and Bennett, KP (2001).
A Linear Programming Approach to Novelty Detection
In: Advances in Neural Information Processing Systems, Vol. 14., vol. 14.

Caputo, B, Hornegger, J, Paulus, D, and Niemann, H (2001).
A Spin-Glass Markov Random Field for 3-D Object Recognition
University of Erlangen.

Cauwenberghs, G and Poggio, T (2001).
Incremental and Decremental Support Vector Machine Learning
In: Advances in Neural Information Processing Systems (NIPS*2000), vol. 13.

Chapelle, O and Scholkopf, B (2001).
Incorporating invariances in non-linear SVMs

Chew, H, Bogner, R, and Lim, C (2001).
Dual nu-Support Vector Machine with Error Rate and Training Size Biasing
In: International Conference on Acoustics, Speech and Signal Processing, ICASSP 2001, USA, pp. to appear.

Chew, H, Bogner, R, and Lim, C (2001).
On Initialising nu-Support Vector Machine Training
In: Proceedings of the 5th International Conference on Optimisation: Techniques and Applications (ICOTA 2001), pp. 1740-1747, Hong Kong.

Collobert, R and Bengio, S (2001).
SVMTorch: Support Vector Machines for Large-Scale Regression Problems
Journal of Machine Learning Research, 1:143-160.

Cristianini, N (2001).
ICML'01 Tutorial
Miscellaneous publication.

Demiriz, A and Bennett, KP (2001).
Optimization Approaches to Semi-supervised Learning
In: Complementarity: Applications, Algorithms and Extensions, ed. by M. C. Ferris and O. L. Mangasarian and J.-S. Pang, pp. 121-141, Boston, Kluwer Academic Publishers.

Demiriz, A, Bennett, KP, and Shawe-Taylor, J (2001).
Linear Programming Boosting via Column Generation
Machine Learning Journal.

Demiriz, A, Bennett, KP, Breneman, CM, and J., M (2001).
Support Vector Machine Regression in Chemometrics
In: Computing Science and Statistics: Proceedings of Interface.

Ding, C and Dubchak, I (2001).
Multi-class protein fold recognition using support vector machines and neural networks
Bioinformatics, 17:349-358.

Evgeniou, T, Pontil, M, and Elisseeff, A (2001).
Leave one out error, stability, and generalization of voting combinations of classifiers
INSEAD Working Paper.

Frontzek, T, Lal, T, and Eckmiller, R (2001).
Predicting the Nonlinear Dynamics of Biological Neurons
In: International Joint Conference on Neural Networks, vol. 2, pp. 1492-1497.

Fung, G, Mangasarian, OL, and Shavlik, J (2001).
Knowledge-Based Support Vector Machine Classifiers
Data Mining Institute, Computer Sciences Department, University of Wisconsin, Madison, Wisconsin.

Girolami, M (2001).
Mercer Kernel Based Clustering in Feature Space
I.E.E.E. Transactions on Neural Networks.

Girolami, M (2001).
Orthogonal Series Density Estimation and the Kernel Eigenvalue Problem
Neural Computation (To Appear).


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