Name
Abstract | ||
|---|---|---|
In earlier publications, we showed that it is possible to achieve both low VC dimension and high accuracy, if we divide the given training set into a sequence of subsets each of which does admit such a solution. Here we explore in substantially more detail how the various steps in what was called ''Margin Setting'' impact false classification and indecision rates. A complex relationship exists between margin size, the number of steps in the process, and those two classification failures. After mapping those relationships, we offer a qualitative explanation of them. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.patcog.2005.01.009 | Pattern Recognition |
Keywords | Field | DocType |
classification failure,low vc dimension,complex relationship,multiple class discrimination,margin setting,exploring margin,indecision rate,good generalization,qualitative explanation,margin size,earlier publication,high accuracy,impact false classification,margin,vc dimension,generalization,pattern recognition,classification | Training set,VC dimension,Margin (machine learning),Artificial intelligence,Class discrimination,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
38 | 8 | Pattern Recognition |
Citations | PageRank | References |
9 | 0.64 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| H. John Caulfield | 1 | 443 | 164.79 |
| Kaveh Heidary | 2 | 10 | 2.00 |