Publications

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

There are 357 references in this bibliography folder.

Opper, M (1999).
On the Annealed VC Entropy for Margin Classifiers: A Statistical Mechanics Study
In: Advances in Kernel Methods — Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 117–126, Cambridge, MA, MIT Press.

Osuna, E and Girosi, F (1999).
Reducing the Run-time Complexity in Support Vector Regression
In: Advances in Kernel Methods — Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 271–284, Cambridge, MA, MIT Press.

Papageorgiou, C and Poggio, T (1999).
A Pattern Classification Approach to Dynamical Object Detection
In: International Conference on Computer Vision ICCV'99, pp. 1223-1228.

Papageorgiou, C and Poggio, T (1999).
Trainable Pedestrian Detection
In: ICIP'99.

Platt, J (1999).
Fast Training of Support Vector Machines using Sequential Minimal Optimization
In: Advances in Kernel Methods — Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 185–208, Cambridge, MA, MIT Press.

Roobaert, D (1999).
Improving the Generalisation of Linear Support Vector Machines: an Application to 3D Object Recognition with Cluttered Background
In: Proc. SVM Workshop at IJCAI'99, Stockholm, Sweden.

Roobaert, D and Hulle, MV (1999).
View-based 3D object recognition with Support Vector Machines
In: IEEE Neural Networks for Signal Processing Workshop.

Rätsch, G, Schökopf, B, Smola, A, Mika, S, Onoda, T, and Müller, K (1999).
Robust Ensemble Learning
In: Advances in Large Margin Classifiers, ed. by A. Smola and P. Bartlett and B. Schölkopf and D. Schuurmans, pp. 207–219, MIT Press, Cambridge, MA.

Sch\"olkopf, B, Bartlett, PL, Smola, A, and Williamson, R (1999).
Shrinking the tube: a new support vector regression algorithm
In: Advances in Neural Information Processing Systems 11, ed. by M. S. Kearns and S. A. Solla and D. A. Cohn, pp. 330 – 336, Cambridge, MA, MIT Press.

Sch\"olkopf, B, Burges, CJ, and Smola, AJ (1999).
Advances in Kernel Methods — Support Vector Learning
MIT Press, Cambridge, MA.

Sch\"olkopf, B, Platt, J, Shawe-Taylor, J, Smola, AJ, and Williamson, RC (1999).
Estimating the Support of a High-Dimensional Distribution
Microsoft Research.

Sch\"olkopf, B, Shawe-Taylor, J, Smola, AJ, and Williamson, RC (1999).
Generalization Bounds via Eigenvalues of the Gram matrix
NeuroCOLT.

Sch\"olkopf, B, Smola, A, and M\"uller, K (1999).
Kernel Principal Component Analysis
In: Advances in Kernel Methods - SV Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 327–352, MIT Press, Cambridge, MA.

Schölkopf, B (1999).
Support Vector Learning
Miscellaneous publication.

Schölkopf, B, Mika, S, Burges, C, Knirsch, P, Müller, K, Rätsch, G, and Smola, A (1999).
Input Space vs. Feature Space in Kernel-Based Methods
IEEE Transactions on Neural Networks, 10(5):1000–1017.

Shawe-Taylor, J and Cristianini, N (1999).
Margin Distribution Bounds on Generalization
In: Proceedings of the European Conference on Computational Learning Theory, EuroCOLT'99, pp. 263–273.

Smola, A, Sch\"olkopf, B, and R\"atsch, G (1999).
Linear programs for automatic accuracy control in regression
In: Ninth International Conference on Artificial Neural Networks, pp. 575 – 580, London, IEE. Conference Publications No. 470.

Smola, AJ (1999).
Lernen mit Kernen
In: Ausgezeichnete Informatikdissertationen, ed. by G. Hotz et al., pp. 184-195, Teubner, Stuttgart.

Smola, AJ, Mangasarian, OL, and Schölkopf, B (1999).
Sparse Kernel Feature Analysis
University of Wisconsin, Data Mining Institute, Madison.

Sollich, P (1999).
Probabilistic interpretation and Bayesian methods for Support Vector Machines
In: Proceedings of ICANN'99, pp. 91-96, IEE Publications.

Sollich, P (1999).
Probabilistic methods for Support Vector Machines
In: NIPS'99, to appear.

Stitson, M, Gammerman, A, Vapnik, V, Vovk, V, Watkins, C, and Weston, J (1999).
Support Vector Regression with ANOVA Decomposition Kernels
In: Advances in Kernel Methods — Support Vector Learning, ed. by B. Schölkopf and C. J. C. Burges and A. J. Smola, pp. 285–292, Cambridge, MA, MIT Press.

Suykens, JA and Vandewalle, J (1999).
Multiclass Least Squares Support Vector Machines
In: IJCNN'99 International Joint Conference on Neural Networks, Washington, DC.

Suykens, JA and Vandewalle, J (1999).
Least squares support vector machine classifiers
Neural Processing Letters, 9(3):293-300.

Suykens, JA, Dooren, PV, Moor, BD, and Vandewalle, J (1999).
Least squares support vector machine classifiers: a large scale algorithm.
In: European Conference on Circuit Theory and Design, ECCTD'99, pp. 839-842.