Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 16,429

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

A Tutorial on Support Vector Machines for Pattern Recognition

Burges, Christopher J.C.·1998·Data Mining and Knowledge Discovery
DOI 10.1023/a:1009715923555
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1023/a:1009715923555

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 6 references

Research works printed but not linked: 41

  1. Aizerman, M.A., Braverman, E.M. and Rozoner, L.I. Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control, 25:821–837, 1964.
  2. Anthony, M. and Biggs, N. Pac learning and neural networks. In The Handbook of Brain Theory and Neural Networks, pages 694–697, 1995.
  3. Blanz, V., Schölkopf, B., Bülthoff, H., Burges, C., Vapnik, V. and Vetter, T. Comparison of view–based object recognition algorithms using realistic 3d models. In C. von der Malsburg, W. von Seelen, J. C. Vorbrüggen, and B. Sendhoff, editors, Artificial Neural Networks—ICANN’96, pages 251–256, Berlin, 1996. Springer Lecture Notes in Computer Science, Vol. 1112. · DOI
  4. Boser, B.E., Guyon, I.M. and Vapnik, V. A training algorithm for optimal margin classifiers. In Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, 1992. ACM. · DOI · Paper page
  5. Bunch, J.R. and Kaufman, L. Some stable methods for calculating inertia and solving symmetric linear systems. Mathematics of computation, 31(137):163–179, 1977. · DOI
  6. Bunch, J.R. and Kaufman, L. A computational method for the indefinite quadratic programming problem. Linear Algebra and its Applications, 34:341–370, 1980. · DOI
  7. Burges, C.J.C. and Schölkopf, B. Improving the accuracy and speed of support vector learning machines. In M. Mozer, M. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, pages 375–381, Cambridge, MA, 1997. MIT Press.
  8. Burges, C.J.C. Simplified support vector decision rules. In Lorenza Saitta, editor, Proceedings of the Thirteenth International Conference on Machine Learning, pages 71–77, Bari, Italy, 1996. Morgan Kaufman.
  9. Burges, C.J.C. Geometry and invariance in kernel based methods. In Advances in Kernel Methods-Support Vector Learning,Bernhard Schölkopf, Christopher J.C. Burges and Alexander J. Smola (eds.), MIT Press, Cambridge, MA, 1998 (to appear). · DOI · Paper page
  10. Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A. and Vapnik, V. Support vector regression machines. Advances in Neural Information Processing Systems, 9:155–161, 1997.
  11. Fletcher, R. Practical Methods of Optimization. John Wiley and Sons, Inc., 2nd edition, 1987.
  12. Girosi, F. An equivalence between sparse approximation and support vector machines. Neural Computation (to appear); CBCL AI Memo 1606, MIT, 1998. · DOI · Paper page
  13. Guyon, I., Vapnik, V., Boser, B., Bottou, L. and Solla. S.A. Structural risk minimization for character recognition. Advances in Neural Information Processing Systems, 4:471–479, 1992.
  14. Horn, R.A. and Johnson, C.R. Matrix Analysis. Cambridge University Press, 1985. · DOI · Paper page
  15. Kaufman, L. Solving the quadratic programming problem arising in support vector classification. In Advances in Kernel Methods-Support Vector Learning,Bernhard Schölkopf, Chrisopher J.C. Burges and Alexander J. Smola (eds.), MIT Press, Cambridge, MA, 1998 (to appear).
  16. Mangarasian, O.L. Nonlinear Programming. McGraw Hill, New York, 1969.
  17. McCormick, G.P. Non Linear Programming: Theory, Algorithms and Applications. John Wiley and Sons, Inc., 1983.
  18. Montgomery, D.C. and Peck, E.A. Introduction to Linear Regression Analysis. John Wiley and Sons, Inc., 2nd edition, 1992.
  19. Moré, J.J. and Toraldo, G. On the solution of large quadratic programming problems with bound constraints. SIAM J. Optimization, 1(1):93–113, 1991. · DOI · Paper page
  20. Mukherjee, S., Osuna, E. and Girosi, F. Nonlinear prediction of chaotic time series using a support vector machine. In Proceedings of the IEEE Workshop on Neural Networks for Signal Processing 7, pages 511–519, Amelia Island, FL, 1997.
  21. Müller, K.-R., Smola, A., Rätsch, G., Schölkopf, B., Kohlmorgen, J. and Vapnik, V. Predicting time series with support vector machines. In Proceedings, International Conference on Artificial Neural Networks, page 999. Springer Lecture Notes in Computer Science, 1997. · DOI · Paper page
  22. Osuna, E., Freund, R. and Girosi, F. An improved training algorithm for support vector machines. In Proceedings of the 1997 IEEE Workshop on Neural Networks for Signal Processing, Eds. J. Principe, L. Giles, N. Morgan, E. Wilson, pages 276–285, Amelia Island, FL, 1997.
  23. Osuna, E., Freund, R. and Girosi, F. Training support vector machines: an application to face detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 130–136, 1997.
  24. Osuna, E. and Girosi. F. Reducing the run-time complexity of support vector machines. In International Conference on Pattern Recognition (submitted), 1998. · DOI
  25. Press, W.H., Flannery, B.P., Teukolsky, S.A. and Vettering, W.T. Numerical recipes in C: the art of scientific computing. Cambridge University Press, 2nd edition, 1992.
  26. Schmidt, M. Identifying speaker with support vector networks. In Interface ’96 Proceedings, Sydney, 1996.
  27. Schölkopf, B. Support Vector Learning. R. Oldenbourg Verlag, Munich, 1997.
  28. Schölkopf, B., Burges, C. and Vapnik, V. Extracting support data for a given task. In U. M. Fayyad and R. Uthurusamy, editors, Proceedings, First International Conference on Knowledge Discovery & Data Mining. AAAI Press, Menlo Park, CA, 1995.
  29. Schölkopf, B., Burges, C. and Vapnik, V. Incorporating invariances in support vector learning machines. In C. von der Malsburg, W. von Seelen, J. C. Vorbrüggen, and B. Sendhoff, editors, Artificial Neural Networks — ICANN’96, pages 47–52, Berlin, 1996. Springer Lecture Notes in Computer Science, Vol. 1112. · DOI · Paper page
  30. Schölkopf, B., Simard, P., Smola, A. and Vapnik, V. Prior knowledge in support vector kernels. In M. Jordan, M. Kearns, and S. Solla, editors, Advances in Neural Information Processing Systems 10, Cambridge, MA, 1998. MIT Press. In press.
  31. Schölkopf, B., Smola, A. and Müller, K.-R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 1998. In press. · DOI · Paper page
  32. Schölkopf, B., Sung, K., Burges, C., Girosi, F., Niyogi, P., Poggio, T. and Vapnik. V. Comparing support vector machines with gaussian kernels to radial basis function classifiers. IEEE Trans. Sign. Processing, 45:2758–2765, 1997. · DOI · Paper page
  33. Shawe-Taylor, J., Bartlett, P.L., Williamson, R.C. and Anthony, M. A framework for structural risk minimization. In Proceedings, 9th Annual Conference on Computational Learning Theory, pages 68–76, 1996. · DOI
  34. Smola, A. and Schölkopf, B. On a kernel-based method for pattern recognition, regression, approximation and operator inversion. Algorithmica (to appear), 1998. · DOI · Paper page
  35. Smola, A., Schölkopf, B. and Müller, K.-R. General cost functions for support vector regression. In Ninth Australian Congress on Neural Networks (to appear), 1998. · DOI
  36. Smola, A.J., Schölkopf, B. and Müller, K.-R. The connection between regularization operators and support vector kernels. Neural Networks (to appear), 1998. · DOI · Paper page
  37. Vanderbei, R.J. Interior point methods: Algorithms and formulations. ORSA J. Computing, 6(1):32–34, 1994. · DOI
  38. Vapnik, V. Estimation of Dependences Based on Empirical Data [in Russian]. Nauka, Moscow, 1979. (English translation: Springer Verlag, New York, 1982).
  39. Vapnik, V. Statistical Learning Theory. John Wiley and Sons, Inc., New York, in preparation.
  40. Vapnik, V., Golowich, S. and Smola, A. Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems, 9:281–287, 1996.
  41. G. Wahba. Support vector machines, reproducing kernel hilbert spaces and the randomized gacv. In Advances in Kernel Methods-Support Vector Learning, Bernhard Schölkopf, Christopher J.C. Burges and Alexander J. Smola (eds.), MIT Press, Cambridge, MA, 1998 (to appear).

Cited by

Metadata

Authors
Burges, C. J. C.
Venue
Data Mining and Knowledge Discovery
Publisher
Springer Science and Business Media LLC
Published
1998-06
Volume
2
Issue
2
Pages
121-167
ISSN
  • 1384-5810 (ISSN-L)
  • 1573-756X
DOI
10.1023/a:1009715923555
OpenAlex
W2139212933
Cited by
16,429
References
6
FWCI
97.58
Type
journal article
Language
en
Open access
closed
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.