Name
Abstract | ||
|---|---|---|
In this brief, a novel method that constructs a sparse kernel machine is proposed. The proposed method generates attractors as sparse solutions from a built-in kernel machine via a dynamical system framework. By readjusting the corresponding coefficients and bias terms, a sparse kernel machine that approximates a conventional kernel machine is constructed. The simulation results show that the constructed sparse kernel machine improves the efficiency of testing phase while maintaining comparable test error. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TNN.2009.2014059 | IEEE Transactions on Neural Networks |
Keywords | Field | DocType |
dynamical system framework,novel method,conventional kernel machine,sparse kernel machine,bias term,sparse solution,corresponding coefficient,comparable test error,built-in kernel machine,analysis of variance,learning artificial intelligence,plasmas,support vector machine,function approximation,kernel,support vector machines,algorithms,information analysis,kernel method,neural networks,artificial intelligence,dynamic system,mutual information,dynamical systems,computer simulation,efficiency,attractors,kernel machine | Pattern recognition,Radial basis function kernel,Computer science,Kernel embedding of distributions,Tree kernel,Kernel principal component analysis,Polynomial kernel,Artificial intelligence,String kernel,Kernel method,Variable kernel density estimation,Machine learning | Journal |
Volume | Issue | ISSN |
20 | 4 | 1941-0093 |
Citations | PageRank | References |
19 | 0.79 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daewon Lee | 1 | 989 | 58.67 |
| Kyu-Hwan Jung | 2 | 82 | 4.82 |
| Jaewook Lee | 3 | 735 | 50.24 |