Abstract
Linear discriminant functions (LDFs) have been successfully used in pattern classification. Perceptrons and Support Vector Machines (SVMs) are two well-known members of the category of linear discriminant functions that have been popularly used in classification. In this chapter, we introduce the notion of linear discriminant function and some of the important properties associated with it.
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
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer (2006)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley (1970)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press (2013)
Zhao, W., Chellappa, R., Nandhakumar, N.: Empirical performance analysis of linear discriminant classifiers, In: Proceedings of Computer Vision and Pattern Recognition, 25–28 June 1998, pp. 164–169. Santa Barbara, CA, USA (1998)
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 Discriminant Function. In: Support Vector Machines and Perceptrons. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-41063-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-41063-0_2
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)