Name
Abstract | ||
|---|---|---|
In this paper, we review thek-Fold Cross Valida- tion (KCV) technique, applied to the Support Vector Machine (SVM) classification algorithm. We compare several varia- tions on the KCV technique: some of them are often used by practitioners, but without any theoretical justification, while others are less used but more rigorous in finding a correct classifier. The last ones allow to establish an upper - bound of the error rate of the SVM, which represent a way to guarantee, in a statistical sense, the reliability of the classifier and, therefore, turns out to be quite importan t in many real-world applications. Some experimental result s on well-known benchmarking datasets allow to perform the comparison and support our claims. |
Year | Venue | Keywords |
|---|---|---|
2009 | DMIN | k-fold cross validation,support vector machine,model selection,error rate,upper bound |
Field | DocType | Citations |
Data mining,Computer science,Word error rate,Support vector machine,Artificial intelligence,Relevance vector machine,Classifier (linguistics),Cross-validation,Bayes error rate,Machine learning,Benchmarking | Conference | 14 |
PageRank | References | Authors |
0.80 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Davide Anguita | 1 | 1001 | 70.58 |
| Alessandro Ghio | 2 | 667 | 35.71 |
| Sandro Ridella | 3 | 677 | 140.62 |
| Dario Sterpi | 4 | 16 | 1.87 |