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 |
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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
TY - GEN
T1 - Comparing linear discriminant analysis and support vector machines
AU - Gokcen, Ibrahim
AU - Peng, Jing
PY - 2002/1/1
Y1 - 2002/1/1
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.
UR - http://www.scopus.com/inward/record.url?scp=65449149606&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:65449149606
SN - 3540000097
SN - 9783540000099
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 104
EP - 113
BT - Advances in Information Systems - Second International Conference, ADVIS 2002 Izmir, Turkey, October 23-25, 2002 Proceedings
A2 - Yakhno, Tatyana
PB - Springer Verlag
ER -