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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.

Herbrich, R, Graepel, T, Bollmann–Sdorra, P, and Obermayer, K (1998).
Learning a Preference Relation in IR
In: Proceedings Workshop Text Categorization and Machine Learning, International Conference on Machine Learning 1998, pp. 80–84.

Joachims, T (1998).
Text Categorization with Support Vector Machines: Learning with Many Relevant Features
In: Proceedings of the European Conference on Machine Learning, ed. by Claire N\'edellec and C\'eline Rouveirol, pp. 137–142, Berlin, Springer.

Kwok, JT (1998).
Support Vector Mixture for Classification and Regression Problems.
In: Proceedings of the 14th International Conf. on Pattern Recognition, Brisbane, Australia.

Lin, X (1998).
Smoothing Spline Analysis of Variance for Polychotomous Response Data
Department of Statistics, University of Wisconsin, Madison WI.

Mangasarian, OL (1998).
Generalized Support Vector Machines
University of Wisconsin, Computer Sciences Department, Madison, WI, USA, Madison.

Mangasarian, OL and Musicant, DR (1998).
Successive overrelaxation for support vector machines
University of Wisconsin, Computer Sciences Department, Madison, WI, USA.

Mattera, D and Palmieri, F (1998).
Support Vector Machines for nonparametric binary hypothesis testing
In: Proceedings of WIRN'98 Vietri sul Mare.

Osuna, E and Girosi, F (1998).
Reducing run-time complexity in SVMs
In: Proceedings of the 14th International Conf. on Pattern Recognition, Brisbane, Australia.

Papageorgiou, C, Evgeniou, T, and Poggio, T (1998).
A Trainable Pedestrian Detection System
In: IEEE Conference on Intelligent Vehicles, 1998, pp. 241-246.

Papageorgiou, C, Oren, M, and Poggio, T (1998).
A General Framework for Object Detection
In: International Conference on Computer Vision ICCV'98.

Pontil, M (1998).
Study and Application of Statistical Learning Theory
Ph.D. Thesis, University of Genova, Italy, Dipartimento di Fisica dell'Univerit\`a di Genova.

Pontil, M and Verri, A (1998).
Support Vector Machines for 3-D Object Recognition
IEEE Trans. PAMI, 20:637–646.

Pontil, M, Mukherjee, S, and Girosi, F (1998).
On the noise model of support vector machine regression
MIT Artificial Intelligence Laboratory, A.I. Memo(1651).

Pontil, M, Rogai, S, and Verri, A (1998).
Support Vector Machines: a Large Scale QP
In: High Performance Algorithms and Software in Nonlinear Optimization, ed. by R. De Leone et al., pp. 315–336, Kluwer Academic Publishers.

Saunders, C, Stitson, MO, Weston, J, Bottou, L, Schölkopf, B, and Smola, A (1998).
Support Vector Machine — Reference Manual
Department of Computer Science, Royal Holloway, University of London, Egham, UK.

Sch\"olkopf, B (1998).
Support-Vektor-Lernen
In: Ausgezeichnete Informatikdissertationen, ed. by G. Hotz et al., pp. 135-150, Teubner, Stuttgart.

Sch\"olkopf, B, Bartlett, P, Smola, A, and Williamson, R (1998).
Support Vector Regression with Automatic Accuracy Control
In: Proceedings of ICANN'98, ed. by L. Niklasson and M. Bod\'en and T. Ziemke, pp. 111–116, Berlin, Springer Verlag. Perspectives in Neural Computing.

Sch\"olkopf, B, Knirsch, P, Smola, A, and Burges, C (1998).
Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as Approximation in Feature Spaces
In: Mustererkennung 1998 — 20. DAGM-Symposium, ed. by P. Levi and M. Schanz and R.-J. Ahlers and F. May, pp. 124 - 132, Berlin, Springer. Informatik aktuell.

Sch\"olkopf, B, Mika, S, Smola, A, R\"atsch, G, and M\"uller, K (1998).
Kernel PCA Pattern Reconstruction via Approximate Pre-Images
In: Proceedings of the 8th International Conference on Artificial Neural Networks, ed. by L. Niklasson and M. Bod\'en and T. Ziemke, pp. 147–152, Berlin, Springer Verlag. Perspectives in Neural Computing.

Sch\"olkopf, B, Smola, A, and M\"uller, K (1998).
Nonlinear component analysis as a kernel Eigenvalue problem
Neural Computation, 10:1299–1319.

Sch\"olkopf, B, Smola, A, M\"uller, K, Burges, C, and Vapnik, V (1998).
Learning and feature extraction with support vector methods
In: Proc. of the Ninth Australian Conf. on Neural Networks, ed. by T. Downs and M. Frean and M. Gallagher, Brisbane, Australia, University of Queensland.

Sch\"olkopf, B, Smola, A, Williamson, R, and Bartlett, PL (1998).
New Support Vector Algorithms
Royal Holloway College, NeuroCOLT Technical Report(NC-TR-98-031), University of London, UK.

Schölkopf, B, Simard, PY, Smola, AJ, and Vapnik, VN (1998).
Prior Knowledge in Support Vector Kernels
In: Advances in Neural information processings systems, ed. by M. I. Jordan and M. J. Kearns and S. A. Solla, vol. 10, pp. 640–646, Cambridge, MA, MIT Press.

Shawe-Taylor, J and Cristianini, N (1998).
Robust bounds on the generalization from the margin distribution
Royal Holloway College, NeuroCOLT Technical Report(NC-TR-98-029), University of London, UK.

Smola, A, Murata, N, Sch\"olkopf, B, and M\"uller, K (1998).
Asymptotically Optimal Choice of $\varepsilon$-Loss for Support Vector Machines
In: Proceedings of ICANN'98, ed. by L. Niklasson and M. Bod\'en and T. Ziemke, pp. 105–110, Berlin, Springer Verlag. Perspectives in Neural Computing.

 

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