Name
Abstract | ||
|---|---|---|
The paper presents a new framework for complex support vector regression (SVR) as well as Support Vector Machines (SVM) for quaternary classification. The method exploits the notion of widely linear estimation to model the input-out relation for complex-valued data and considers two cases: 1) the complex data are split into their real and imaginary parts and a typical real kernel is employed to ma... |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TNNLS.2014.2336679 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Kernel,Support vector machines,Estimation,Calculus,Hilbert space,Vectors | Pattern recognition,Radial basis function kernel,Least squares support vector machine,Kernel embedding of distributions,Computer science,Support vector machine,Polynomial kernel,Artificial intelligence,Kernel method,String kernel,Variable kernel density estimation,Machine learning | Journal |
Volume | Issue | ISSN |
26 | 6 | 2162-237X |
Citations | PageRank | References |
4 | 0.42 | 34 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pantelis Bouboulis | 1 | 171 | 11.05 |
| Sergios Theodoridis | 2 | 1353 | 106.97 |
| Charalampos Mavroforakis | 3 | 46 | 4.59 |
| Leoni Evaggelatou-Dalla | 4 | 4 | 0.42 |