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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.

Sch\"olkopf, B (1997).
Support Vector Learning
R. Oldenbourg Verlag, Munich.

Sch\"olkopf, B, Sung, K, Burges, C, Girosi, F, Niyogi, P, Poggio, T, and Vapnik, V (1997).
Comparing support vector machines with Gaussian kernels to radial basis function classifiers
IEEE Trans. Sign. Processing, 45:2758 - 2765.

Vapnik, V, Golowich, S, and Smola, A (1997).
Support vector method for function approximation, regression estimation, and signal processing
In: Advances in Neural Information Processing Systems 9, ed. by M. Mozer and M. Jordan and T. Petsche, pp. 281–287, Cambridge, MA, MIT Press.

Wahba, G (1997).
Support Vector Machines, Reproducing Kernel Hilbert Spaces and the Randomized GACV
Department of Statistics, University of Wisconsin, Madison.

Williams, CK (1997).
Computing with infinite networks
In: Advances in Neural Information Processing Systems 9, ed. by Mozer, M. C. and Jordan, M. I. and Petsche, T., MIT Press.

Blanz, V, Sch\"olkopf, B, B\"ulthoff, H, Burges, C, Vapnik, V, and Vetter, T (1996).
Comparison of view–based object recognition algorithms using realistic 3D models
In: Artificial Neural Networks — ICANN'96, ed. by C. von der Malsburg and W. von Seelen and J. C. Vorbrüggen and B. Sendhoff, pp. 251 - 256, Berlin, Springer Lecture Notes in Computer Science, Vol. 1112.

Burges, CJ (1996).
Simplified support vector decision rules
In: Proc. 13th International Conference on Machine Learning, ed. by L. Saitta, pp. 71–77, San Mateo, CA, Morgan Kaufmann.

Guyon, I, Matic, N, and Vapnik, V (1996).
Discovering informative patterns and data cleaning.
In: Advances in Knowledge Discovery and Data Mining, ed. by U.M. Fayyad and G. Piatetsky-Shapiro and P. Smyth and R. Uthurusamy, pp. 181–203.

Osuna, E, Freund, R, and Girosi, F (1996).
Support Vector Machines: Training and Applications
MIT A.I. Lab..

Sch\"olkopf, B (1996).
K\"unstliches Lernen
In: Komplexe adaptive Systeme (Forum f\"ur Interdisziplin\"are Forschung Bd. 15), ed. by S. Bornholdt and P. H. Feindt, pp. 93 - 117, R\"oll, Dettelbach.

Sch\"olkopf, B, Burges, C, and Vapnik, V (1996).
Incorporating Invariances in Support Vector Learning Machines
In: Artificial Neural Networks — ICANN'96, ed. by C. von der Malsburg and W. von Seelen and J. C. Vorbrüggen and B. Sendhoff, pp. 47 - 52, Berlin, Springer Lecture Notes in Computer Science, Vol. 1112.

Smola, AJ (1996).
Regression Estimation with Support Vector Learning Machines
Master thesis, Technische Universität München.

Williams, CK and Rasmussen, CE (1996).
Gaussian processes for regression
In: Advances in Neural Information Processing Systems 8, ed. by Touretzky, D. S. and Mozer, M. C. and Hasselmo, M. E., pp. 514-520, MIT Press.

Cortes, C (1995).
Prediction of Generalization Ability in Learning Machines
PhD thesis, Department of Computer Science, University of Rochester.

Cortes, C and Vapnik, V (1995).
Support Vector Networks
Machine Learning, 20:273 - 297.

Schölkopf, B, Burges, C, and Vapnik, V (1995).
Extracting support data for a given task
In: Proceedings, First International Conference on Knowledge Discovery & Data Mining, ed. by U. M. Fayyad and R. Uthurusamy, Menlo Park, AAAI Press.

Vapnik, V (1995).
The Nature of Statistical Learning Theory
Springer, N.Y. (ISBN: 0-387-94559-8).

Guyon, I, Boser, B, and Vapnik, V (1993).
Automatic Capacity Tuning of Very Large VC-Dimension Classifiers
In: Advances in Neural Information Processing Systems, ed. by Stephen Jos\'e Hanson and Jack D. Cowan and C. Lee Giles, vol. 5, pp. 147–155, Morgan Kaufmann, San Mateo, CA.

Matic, N, Guyon, I, Denker, J, and Vapnik, V (1993).
Writer adaptation for on-line handwritten character recognition.
In: In Second International Conference on Pattern Recognition and Document Analysis, ed. by IEEE Computer Society Press, pp. 187–191, Tsukuba, Japan.

Bennett, K and Mangasarian, O (1992).
Robust Linear Programming Discrimination of Two Linearly Inseparable Sets
Optimization Methods and Software, 1:23–34.

Boser, BE, Guyon, IM, and Vapnik, VN (1992).
A Training Algorithm for Optimal Margin Classifiers
In: 5th Annual ACM Workshop on COLT, ed. by D. Haussler, pp. 144–152, Pittsburgh, PA, ACM Press.

Wahba, G (1990).
Spline Models for Observational Data
Series in Applied Mathematics, Vol. 59, SIAM, Philadelphia.

Vapnik, V (1979).
Estimation of Dependences Based on Empirical Data [in Russian]
Nauka, Moscow.

Vapnik, V and Chervonenkis, A (1974).
Theory of Pattern Recognition [in Russian]
Nauka, Moscow.

Kimeldorf, GS and Wahba, G (1971).
Some results on Tchebycheffian spline functions.
J. Math. Anal. Applications, 33(1):82-95.


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