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.
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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)
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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
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DOI: https://doi.org/10.1007/978-3-319-41063-0_2
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