Name
Abstract | ||
|---|---|---|
We show how to train SVMs with an optimal guarantee on the number of support vectors (up to constants), and with sample complexity and training runtime bounds matching the best known for kernel SVM optimization (i.e. without any additional asymptotic cost beyond standard SVM training). Our method is simple to implement and works well in practice. |
Year | Venue | Field |
|---|---|---|
2013 | ICML | Kernel (linear algebra),Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Sample complexity,Machine learning |
DocType | Citations | PageRank |
Conference | 7 | 0.50 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew Cotter | 1 | 851 | 78.35 |
| Shai Shalev-Shwartz | 2 | 3681 | 276.32 |
| Nathan Srebro | 3 | 3892 | 349.42 |