Abstract
Both Linear Discriminant Analysis and Support Vector Machines compute hyperplanes that are optimal with respect to their individual objectives. However, there can be vast differences in performance between the two techniques depending on the extent to which their respective assumptions agree with problems at hand. In this paper we compare the two techniques analytically and experimentally using a number of data sets. For analytical comparison purposes, a unified representation is developed and a metric of optimality is proposed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bach, F. R., Jordan, M. I.: Kernel Independent Component Analysis. Technical Report No. UCB/CSD-01-1166, UC Berkeley (2001)
Burges, C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2 (1998) 121–167
Cristianini, N., Shewe-Taylor, J.: Support Vector Machines. Cambridge University Press (2000)
Duda, R. O., Hart, P. E.: Pattern Classification and Scene Analysis. John Wiley & Sons Inc., (1973)
Friedman, J. H.: Flexible Metric Nearest Neighbor Classification. Tech. Report, Dept. of Statistics, Stanford University, (1994)
Fukunaga, K.: Statistical Pattern Recognition. Academic Press, (1990)
Hastie, T., Tibshirani, R.: Discriminant Adaptive Nearest Neighbor Classification. IEEE Trans. on Pattern Analysis and Machine Intelligence 18:6 (1996) 607–615
Herbrich, R., Graepel, T., Campbell, C.: Bayes Point Machines. Journal of Machine Learning Research, 1 (2001) 245–279
Joachims, T.: Making large-scale SVM-learning practical. Advances in Kernel Methods—Support Vector Learning, MIT Press, Cambridge, MA (1999) 169–184
Mclachlan, G. J.: Discriminant Analysis and Statistical Pattern Recognition. Wiley, New York, (1992)
Mercer, J.: Functions of positive and negative type and their connection with the theory of integral equations. Philos. Trans. Royal Soc. London, A 209 (1909) 415–446.
Merz, C. J., Murphy, P. M., Aha, D. W.: UCI repository of machine learning databases. Department of Information and Computer Science, University of California at Irvine, Irvine, CA, (1997)
Scholkopf B., Smola, A. J.: Learning with Kernels. MIT Press, Cambridge, MA. (2002)
Vapnik, V. N.: The Nature of Statistical Learning Theory. Springer-Verlag New York Inc. (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gokcen, I., Peng, J. (2002). Comparing Linear Discriminant Analysis and Support Vector Machines. In: Yakhno, T. (eds) Advances in Information Systems. ADVIS 2002. Lecture Notes in Computer Science, vol 2457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36077-8_10
Download citation
DOI: https://doi.org/10.1007/3-540-36077-8_10
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00009-9
Online ISBN: 978-3-540-36077-3
eBook Packages: Springer Book Archive