Comparing linear discriminant analysis and support vector machines

Ibrahim Gokcen, Jing Peng

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

15 Citations (Scopus)

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 languageEnglish
Title of host publicationAdvances in Information Systems - Second International Conference, ADVIS 2002 Izmir, Turkey, October 23-25, 2002 Proceedings
EditorsTatyana Yakhno
PublisherSpringer Verlag
Pages104-113
Number of pages10
ISBN (Print)3540000097, 9783540000099
StatePublished - 1 Jan 2002
Event2nd International Conference on Advances in Information Systems, ADVIS 2002 - Izmir, Turkey
Duration: 23 Oct 200225 Oct 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2457
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Conference on Advances in Information Systems, ADVIS 2002
CountryTurkey
CityIzmir
Period23/10/0225/10/02

Fingerprint

Discriminant analysis
Discriminant Analysis
Support vector machines
Support Vector Machine
Hyperplane
Optimality
Metric

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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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 proceedingConference contributionResearchpeer-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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BT - Advances in Information Systems - Second International Conference, ADVIS 2002 Izmir, Turkey, October 23-25, 2002 Proceedings

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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)).