Comparing Linear Discriminant Analysis and Support Vector Machines

Authors:
Ibrahim Gokcen;Jing Peng
Affiliations:
-;-
Venue:
ADVIS '02 Proceedings of the Second International Conference on Advances in Information Systems
Year:
2002

Citing 7
Cited 0

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory

The nature of statistical learning theory
Discriminant Adaptive Nearest Neighbor Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Making large-scale support vector machine learning practical

Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Kernel Independent Component Analysis

Kernel Independent Component Analysis
Bayes point machines

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

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.