Publications

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

There are 357 references in this bibliography folder.

Zien, A, Rätsch, G, Mika, S, Schölkopf, B, Lengauer, T, and Müller, K (2000).
Engineering Support Vector Machine Kernels that Recognize Translation Initiation Sites
BioInformatics, 16(9):799-807.

Amari, S and Wu, S (1999).
Improving support vector machines by modifying kernel functions
Neural Networks:783-789.

Ancona, N (1999).
Properties of Support Vector Machines for Regression
Istituto Elaborazione Segnali ed Immagini, Bari, Italy.

Ancona, N (1999).
Classification Properties of Support Vector Machines for Regression
Istituto Elaborazione Segnali ed Immagini, Bari, Italy.

Ancona, N (1999).
On margin and support vector separability in Support Vector Machines for Regression
Istituto Elaborazione Segnali ed Immagini, Bari, Italy.

Bartlett, P and Shawe–Taylor, J (1999).
Generalization Performance of Support Vector Machines and Other Pattern Classifiers
In: Advances in Kernel Methods — Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 43–54, Cambridge, MA, MIT Press.

Bennett, K (1999).
Combining Support Vector and Mathematical Programming Methods for Induction
In: Advances in Kernel Methods - SV Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 307–326, Cambridge, MA, MIT Press.

Brown, M, Grundy, W, Lin, D, Cristianini, N, Sugnet, C, Ares, M, and Haussler, D (1999).
Support Vector Machine Classification of Microarray Gene Expression Data
University of California, Santa Cruz.

Burges, CJ (1999).
Geometry and Invariance in Kernel Based Methods
In: Advances in Kernel Methods — Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 89–116, Cambridge, MA, MIT Press.

Chang, C and Lin, C (1999).
Some Analysis on $\nu$-Support Vector Classification
National Taiwan University.

Chang, C, Hsu, C, and Lin, C (1999).
The analysis of decomposition methods for support vector machines
In: Proceeding of IJCAI99, SVM workshop.

Chapelle, O, Haffner, P, and Vapnik, V (1999).
SVMs for Histogram-Based Image Classification
IEEE Transaction on Neural Networks, 9.

Cristianini, N and Shawe–Taylor, J (1999).
Bayesian Voting Schemes and Large Margin Classifiers
In: Advances in Kernel Methods — Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 55–68, Cambridge, MA, MIT Press.

Cristianini, N, Campbell, C, and Shawe-Taylor, J (1999).
Dynamically Adapting Kernels in Support Vector Machines.
In: Advances in Neural Information Processing Systems,, vol. 11, pp. 204-210.

Cristianini, N, Campbell, C, and Shawe-Taylor., J (1999).
Multiplicative Updatings for Support Vector Machines
In: Proceeding of ESANN'99, ed. by D-Facto Publications, pp. 189-194, Belgium.

Dietrich, R, Opper, M, and Sompolinsky, H (1999).
Statistical Mechanics of Support Vector Networks
Physical Review Letters, 82(14):2975–2978.

Evgeniou, T and Pontil, M (1999).
On the V-gamma dimension for regression in Reproducing Kernel Hilbert spaces
In: Lecture Notes in Computer Science, Algorithmic Learning Theory, Tokyo, Japan.

Ferrari Trecate, G, Williams, CK, and Opper, M (1999).
Finite-dimensional approximation of Gaussian processes
In: Advances in Neural Information Processing Systems 11, ed. by Kearns, M. S. and Solla, S. A. and Cohn, D. A., pp. 218-224, MIT Press.

Graepel, T, Herbrich, R, and Obermayer, K (1999).
Classification on Pairwise Proximity Data
In: NIPS, ed. by M. I. Jordan and M. J. Kearns and S. A. Solla, vol. 11, MIT Press, Cambridge, MA.

Graepel, T, Herbrich, R, and Obermayer, K (1999).
Bayesian Transduction
In: Advances in Neural Information System Processing.

Graepel, T, Herbrich, R, Bollmann–Sdorra, P, and Obermayer, K (1999).
Classification on Pairwise Proximity Data
In: Advances in Neural Information System Processing, pp. 438–444.

Graepel, T, Herbrich, R, Sch\"olkopf, B, Smola, AJ, Bartlett, P, M\"uller, K, Obermayer, K, and Williamson, RC (1999).
Classification on proximity data with LP-machines
In: Ninth International Conference on Artificial Neural Networks, pp. 304 – 309, London, IEE. Conference Publications No. 470.

Gunn, S and Brown, M (1999).
SUPANOVA - A Sparse, Transparent Modelling Approach
In: Proc. IEEE Neural Networks for Signal Processing Workshop, IEEE Press.

Herbrich, R and Weston, J (1999).
Adaptive Margin Support Vector Machines for Classification Learning
In: Proceedings of the Ninth International Conference on Artificial Neural Networks, pp. 880–885.

Herbrich, R, Graepel, T, and Campbell, C (1999).
Bayes Point Machines: Estimating the Bayes Point in Kernel Space
In: Proceedings of IJCAI Workshop Support Vector Machines, pp. 23–27.