Abstract
Kernel Support Vector Machine (SVM) is useful to deal with nonlinear classification based on a linear discriminant function in a high-dimensional (kernel) space. Linear SVM is popularly used in applications involving high-dimensional spaces. However, in low-dimensional spaces, kernel SVM is a popular nonlinear classifier. It employs kernel trick which permits us to work in the input space instead of dealing with a potentially high-dimensional, even theoretically infinite dimensional, kernel (feature) space. Also kernel trick has become so popular that it is used in a variety of other pattern recognition and machine learning algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Asharaf, S., Murty, M.N., Shevade, S.K.: Multiclass core vector machine. In: Proceedings of International Conference on Machine Learning, 20–24 June 2007, pp. 41–48. Corvallis, Oregon, USA (2007)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2(2), 121–167 (1998)
Rifkin, R.M.: Multiclass Classification, Lecture Notes, Spring08. MIT, USA (2008)
Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press (2001)
Vishwanathan, S.V.N., Smola, A.J., Murty, M.N.: Simple SVM. In: Proceedings of International Conference on Machine Learning, 21–24 August 2003, pp. 760–767. Washington, D.C., USA (2003)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining. Third Edition, Morgan Kauffmann (2011)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2016 The Author(s)
About this chapter
Cite this chapter
Murty, M.N., Raghava, R. (2016). Kernel-Based SVM. In: Support Vector Machines and Perceptrons. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-41063-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-41063-0_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41062-3
Online ISBN: 978-3-319-41063-0
eBook Packages: Computer ScienceComputer Science (R0)