Name
Abstract | ||
|---|---|---|
Multiple kernel learning (MKL) is a widely used kernel learning method, but how to select kernel is lack of theoretical guidance. The performance of MKL is depend on the users' experience, which is difficult to choose the proper kernels in practical applications. In this paper, we propose a MKL method based on minimal redundant maximal relevance criterion and kernel alignment. The main feature of this method compared to others in the literature is that the selection of kernels is considered as a feature selection issue in the Hilbert space, and can obtain a set of base kernels with the highest relevance to the target task and the minimal redundancies among themselves. Experimental results on several benchmark classification data sets show that our proposed method can enhance the performance of MKL. © Springer-Verlag 2013. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-42054-2_15 | ICONIP (1) |
Keywords | Field | DocType |
kernel alignment,kernel selection,minimal redundant maximal relevance,multiple kernel learning | Kernel (linear algebra),Hilbert space,Data set,Radial basis function kernel,Feature selection,Pattern recognition,Computer science,Multiple kernel learning,Polynomial kernel,Artificial intelligence,Kernel method,Machine learning | Conference |
Volume | Issue | ISSN |
8226 LNCS | PART 1 | 16113349 |
Citations | PageRank | References |
2 | 0.37 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Wu | 1 | 2 | 0.37 |
| Fuqing Duan | 2 | 163 | 26.40 |
| Ping Guo | 3 | 601 | 85.05 |