| Bibtype | Inproceedings |
|---|---|
| Bibkey | Pfahler/Morik/2018a |
| Author | Pfahler, Lukas and Morik, Katharina |
| Ls8autor |
Morik, Katharina
Pfahler, Lukas |
| Title | Nystroem-SGD: Rapidly Learning Kernel-Classifiers with Conditioned Stochastic Gradient Descent |
| Booktitle | Machine Learning and Knowledge Discovery in Databases - European Conference, {ECML} {PKDD} 2018, Dublin, Ireland |
| Abstract | Kernel methods are a popular choice for classification problems, but when solving large-scale learning tasks computing the quadratic kernel matrix quickly becomes infeasible. To circumvent this problem, the Nyström method that approximates the kernel matrix using only a smaller sample of the kernel matrix has been proposed. Other techniques to speed up kernel learning include stochastic first order optimization and conditioning.We introduce Nyström-SGD, a learning algorithm that trains kernel classifiers by minimizing a convex loss function with conditioned stochastic gradient descent while exploiting the low-rank structure of a Nyström kernel approximation.Our experiments suggest that the Nyström-SGD enables us to rapidly train high-accuracy classifiers for large-scale classification tasks. |
| Year | 2018 |
| Projekt | SFB876-C3 |