Name
Title | ||
|---|---|---|
Reliability of Cross-Validation for SVMs in High-Dimensional, Low Sample Size Scenarios |
Abstract | ||
|---|---|---|
A Support-Vector-Machine (SVM) learns for given 2-class-data a classifier that tries to achieve good generalisation by maximising the minimal margin between the two classes. The performance can be evaluated using cross-validation testing strategies. But in case of low sample size data, high dimensionality might lead to strong side-effects that can significantly bias the estimated performance of the classifier. On simulated data, we illustrate the effects of high dimensionality for cross-validation of both hard- and soft-margin SVMs. Based on the theoretical proofs towards infinity we derive heuristics that can be easily used to validate whether or not given data sets are subject to these constraints. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-87536-9_5 | ICANN (1) |
Keywords | Field | DocType |
cross-validation testing strategy,low sample size scenarios,strong side-effects,simulated data,estimated performance,minimal margin,good generalisation,soft-margin svms,low sample size data,high dimensionality,support vector machine,side effect,cross validation,sample size | Data set,Pattern recognition,Computer science,Generalization,Support vector machine,Curse of dimensionality,Heuristics,Artificial intelligence,Classifier (linguistics),Cross-validation,Sample size determination,Machine learning | Conference |
Volume | ISSN | Citations |
5163 | 0302-9743 | 7 |
PageRank | References | Authors |
0.73 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sascha Klement | 1 | 24 | 3.26 |
| Amir Madany Mamlouk | 2 | 37 | 9.52 |
| Thomas Martinetz | 3 | 1462 | 231.48 |