Supervised Learning by Support Vector Machines

Gabriele Steidl

Chapter 22 in Handbook of Mathematical Methods in Imaging, 2011, pp 959-1013 from Springer

Abstract: Abstract During the last 2 decades support vector machine learning has become a very active field of research with a large amount of both sophisticated theoretical results and exciting real-word applications. This chapter gives a brief introduction into the basic concepts of supervised support vector learning and touches some recent developments in this broad field.

Keywords: Support Vector Machine; Loss Function; Support Vector Regression; Dual Problem; Sparse Representation (search for similar items in EconPapers)
Date: 2011
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-0-387-92920-0_22

Ordering information: This item can be ordered from
http://www.springer.com/9780387929200

DOI: 10.1007/978-0-387-92920-0_22

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().