Skip to main content
Book cover

Compstat pp 225–230Cite as

Classification Based on the Support Vector Machine, Regression Depth, and Discriminant Analysis

  • Conference paper

Abstract

The minimum number of misclassifications achievable with affine hyperplanes on a given set of labeled points is a key quantity in both statistics and computational learning theory. We compare the modern approaches the regression depth method and the support vector machine with discrimimant analysis. Summarizing, the regression depth method using currently available algorithms yields often better classifications results for small to moderate data sets, say for sample sizes less than 1000 and dimension up to 10, whereas the support vector machine is often more appropriate for larger or higher dimensional data mining problems.

Keywords

  • Data mining
  • Discriminant analysis
  • Logstic regression
  • Overlap
  • Regression depth
  • Separation
  • Support vector machine

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-57489-4_30
  • Chapter length: 6 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-57489-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   219.00
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Christmann, A., Fischer, P., Joachims, T. (2002). Comparison between the regression depth method and the support vector machine to approximate the minimum number of misclassifications. To appear in: Computational Statistics

    Google Scholar 

  • Christmann, A. and Rousseeuw, P.J. (2001). Measuring overlap in logistic regression. Computational Statistics and Data Analysis, 37, 65–75

    MathSciNet  MATH  CrossRef  Google Scholar 

  • Hastie, T., Tibshirani, R., Friedman, J. (2001). The Elements of Statistical Learning. Data Mining, Inference and Prediction New York: Springer.

    Google Scholar 

  • Höffgen, K.U., Simon, H.-U., van Horn, K.S. (1995). Robust Trainability of Single Neurons.J. Computer and System Sciences,50, 114–125.

    MATH  CrossRef  Google Scholar 

  • Joachims, T. (1999). Making large-Scale SVM Learning Practical. In: B. Schölkopf, C. Burges, A.Smola (ed.),Advances in Kernel Methods - Support Vector LearningMIT-Press

    Google Scholar 

  • Meyer, D. (2001). Support Vector Machines. The Interface to libsvm in package e1071 Online-Documentation of the package e1071 for “R”.

    Google Scholar 

  • Rousseeuw, P.J. and Hubert, M. (1999). Regression Depth. J. Amer. Statist. Assoc, 94, 388–433.

    MathSciNet  MATH  CrossRef  Google Scholar 

  • Rousseeuw, P.J. and Struyf, A. (1998). Computing location depth and regres-sion depth in higher dimensions. Statistics and Computing, 8, 193–203.

    CrossRef  Google Scholar 

  • Schölkopf, B. and Smola, A.J. Learning with Kernels. Support Vector Ma- chines Regularization, Optimization, and Beyond. MIT Press, 2002.

    Google Scholar 

  • Vapnik, V. (1998). Statistical Learning Theory Wiley, New York.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Christmann, A., Fischer, P., Joachims, T. (2002). Classification Based on the Support Vector Machine, Regression Depth, and Discriminant Analysis. In: Härdle, W., Rönz, B. (eds) Compstat. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57489-4_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-57489-4_30

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1517-7

  • Online ISBN: 978-3-642-57489-4

  • eBook Packages: Springer Book Archive