Name
Abstract | ||
|---|---|---|
Extreme learning machine, which has recently lead to gain popularity of single hidden layer feed-forward neural networks, provides a key solution for non-linear problems with least norm and least square solutions at a very low run time. In this work, it is intended to increase the success of hyperspectral image classification with using kernel extreme learning machine. For this purpose, a hybrid kernel is proposed by the convex combination of radial base and polynomial base kernels. In the simulations, Indian Pine hyperspectral image is used and obtained classification results of proposed method are presented with different kernels' results besides results of non-kernel extreme learning machines. |
Year | Venue | Keywords |
|---|---|---|
2017 | Signal Processing and Communications Applications Conference | hyperspectral imaging,extreme learning machine,hybrid kernels,classification |
Field | DocType | ISSN |
Radial basis function kernel,Extreme learning machine,Computer science,Hybrid kernel,Tree kernel,Polynomial kernel,Artificial intelligence,Artificial neural network,Contextual image classification,Computer vision,Pattern recognition,Kernel embedding of distributions,Machine learning | Conference | 2165-0608 |
Citations | PageRank | References |
2 | 0.36 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ugur Ergul | 1 | 3 | 2.74 |
| Gökhan Bilgin | 2 | 62 | 13.18 |