Name
Abstract | ||
|---|---|---|
The imbalanced nature of some real-world data is one of the current challenges for machine learning researchers. One common approach oversamples the minority class through convex combination of its patterns. We explore the general idea of synthetic oversampling in the feature space induced by a kernel function (as opposed to input space). If the kernel function matches the underlying problem, the ... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TNNLS.2015.2461436 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Kernel,Support vector machines,Training,Symmetric matrices,Learning systems,Eigenvalues and eigenfunctions,Algorithm design and analysis | Graph kernel,Feature vector,Pattern recognition,Radial basis function kernel,Computer science,Kernel embedding of distributions,Polynomial kernel,Artificial intelligence,String kernel,Kernel method,Machine learning,Kernel (statistics) | Journal |
Volume | Issue | ISSN |
27 | 9 | 2162-237X |
Citations | PageRank | References |
8 | 0.44 | 31 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| María Pérez-Ortiz | 1 | 61 | 12.51 |
| Pedro Antonio Gutiérrez | 2 | 433 | 47.30 |
| Tino P. | 3 | 1606 | 155.22 |
| César Hervás-Martínez | 4 | 796 | 78.92 |