Support Vector Machine

  • Reference work entry
Encyclopedia of Database Systems

Synonyms

SVM

Definition

Support vector machines (SVMs) represent a set of supervised learning techniques that create a function from training data. The training data usually consist of pairs of input objects (typically vectors) and desired outputs. The learned function can be used to predict the output of a new object. SVMs are typically used for classification where the function outputs one of finite classes. SVMs are also used for regression and preference learning, for which they are called support vector regression (SVR) and ranking SVM, respectively. SVMs belong to a family of generalized linear classifier where the classification (or boundary) function is a hyperplane in the feature space. Two special properties of SVMs are that SVMs achieve (i) high generalization (Generalization denotes the performance of the learned function on testing data or “unseen” data that are excluded in training.) by maximizing the margin (Margin denotes the distance between the hyperplane and the...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 1,979.50
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Recommended Reading

  1. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discovery, 2:121–167, 1998.

    Google Scholar 

  2. Hastie T. and Tibshirani R. Classification by pairwise coupling. In Advances in Neural Information Processing Systems, 1998.

    Google Scholar 

  3. R., Herbrich T., and Graepel K. (eds.) Obermayer Large margin rank boundaries for ordinal regression. MIT Press, Cambridge, MA, 2000.

    Google Scholar 

  4. Smola A.J. and Scholkopf B. A tutorial on support vector regression. Technical Report, NeuroCOLT2 Technical Report NC2-TR-1998-030, 1998.

    Google Scholar 

  5. Yu H. SVM selective sampling for ranking with application to data retrieval. In Proc. 11th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2005.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this entry

Cite this entry

Yu, H. (2009). Support Vector Machine. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_557

Download citation

Publish with us

Policies and ethics

Navigation