Abstract
Support vector machine (SVM) is the most popular classifier based on a linear discriminant function. It is ideally suited for binary classification. It has been studied extensively in several pattern recognition applications and in data mining. It has become a baseline standard for classification because of excellent software packages that have been developed systematically over the past three decades. In this chapter, we introduce SVM-based classification and some of the essential properties related to classification. Specifically we deal with linear SVM that is ideally suited to deal with linearly separable classes.
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
References
Fan, R.-E., Chang, K.-W., Hsieh, C.-J, Wang, X.-R., Lin, C.-J.: LIBLINEAR: a library for large linear classification. JMLR 9, 1871–1874 (2008)
Hsu, C.W., Lin, C.-J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks 13(2), 415–425 (2002)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)
Rifkin, R.M.: Multiclass Classification, Lecture Notes, Spring08. MIT, USA (2008)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining, 3rd edn. 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). Linear Support Vector Machines. In: Support Vector Machines and Perceptrons. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-41063-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-41063-0_4
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)