Comparing linear discriminant analysis and support vector machines

Ibrahim Gokcen; Jing Peng

Comparing linear discriminant analysis and support vector machines

Ibrahim Gokcen, Jing Peng

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

20 Scopus citations

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.

Original language	English
Title of host publication	Advances in Information Systems - Second International Conference, ADVIS 2002 Izmir, Turkey, October 23-25, 2002 Proceedings
Editors	Tatyana Yakhno
Publisher	Springer Verlag
Pages	104-113
Number of pages	10
ISBN (Print)	3540000097, 9783540000099
State	Published - 1 Jan 2002
Event	2nd International Conference on Advances in Information Systems, ADVIS 2002 - Izmir, Turkey Duration: 23 Oct 2002 → 25 Oct 2002

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2457
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	2nd International Conference on Advances in Information Systems, ADVIS 2002
Country	Turkey
City	Izmir
Period	23/10/02 → 25/10/02

Cite this

Gokcen, I., & Peng, J. (2002). Comparing linear discriminant analysis and support vector machines. In T. Yakhno (Ed.), Advances in Information Systems - Second International Conference, ADVIS 2002 Izmir, Turkey, October 23-25, 2002 Proceedings (pp. 104-113). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2457). Springer Verlag.

Gokcen, Ibrahim ; Peng, Jing. / Comparing linear discriminant analysis and support vector machines. Advances in Information Systems - Second International Conference, ADVIS 2002 Izmir, Turkey, October 23-25, 2002 Proceedings. editor / Tatyana Yakhno. Springer Verlag, 2002. pp. 104-113 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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Gokcen, I & Peng, J 2002, Comparing linear discriminant analysis and support vector machines. in T Yakhno (ed.), Advances in Information Systems - Second International Conference, ADVIS 2002 Izmir, Turkey, October 23-25, 2002 Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2457, Springer Verlag, pp. 104-113, 2nd International Conference on Advances in Information Systems, ADVIS 2002, Izmir, Turkey, 23/10/02.

Comparing linear discriminant analysis and support vector machines. / Gokcen, Ibrahim; Peng, Jing.

Advances in Information Systems - Second International Conference, ADVIS 2002 Izmir, Turkey, October 23-25, 2002 Proceedings. ed. / Tatyana Yakhno. Springer Verlag, 2002. p. 104-113 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2457).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Gokcen I, Peng J. Comparing linear discriminant analysis and support vector machines. In Yakhno T, editor, Advances in Information Systems - Second International Conference, ADVIS 2002 Izmir, Turkey, October 23-25, 2002 Proceedings. Springer Verlag. 2002. p. 104-113. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Comparing linear discriminant analysis and support vector machines

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