Name
Title | ||
|---|---|---|
SVM-Maj: a majorization approach to linear support vector machines with different hinge errors |
Abstract | ||
|---|---|---|
Support vector machines (SVM) are becoming increasingly popular for the prediction of a binary dependent variable. SVMs perform
very well with respect to competing techniques. Often, the solution of an SVM is obtained by switching to the dual. In this
paper, we stick to the primal support vector machine problem, study its effective aspects, and propose varieties of convex
loss functions such as the standard for SVM with the absolute hinge error as well as the quadratic hinge and the Huber hinge
errors. We present an iterative majorization algorithm that minimizes each of the adaptations. In addition, we show that many
of the features of an SVM are also obtained by an optimal scaling approach to regression. We illustrate this with an example
from the literature and do a comparison of different methods on several empirical data sets. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s11634-008-0020-9 | Advanced Data Analysis and Classification |
Keywords | Field | DocType |
support vector machines · iterative majorization · absolute hinge error · quadratic hinge error · huber hinge error · optimal scaling,support vector machines | Mathematical optimization,Hinge loss,Support vector machine,Quadratic equation,Algorithm,Majorization,Variables,Huber loss,Hinge,Mathematics,Binary number | Journal |
Volume | Issue | ISSN |
2 | 1 | 1862-5355 |
Citations | PageRank | References |
1 | 0.58 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patrick J. F. Groenen | 1 | 84 | 11.72 |
| Georgi I. Nalbantov | 2 | 4 | 3.56 |
| Jan C. Bioch | 3 | 217 | 40.62 |