Name
Title | ||
|---|---|---|
Variance analyses for kernel regressors with nested reproducing kernel hilbert spaces |
Abstract | ||
|---|---|---|
Learning based on kernel machines is widely known as a powerful tool for various fields of information science including signal processing such as function estimation from finite sampling points. One of central topics of kernel machines is model selection, especially selection of a kernel or its parameters. In our previous works, we investigated the generalization error of a model space itself corresponding to a selected kernel in kernel regressors. In this paper, we discuss the generalization error in a model space corresponding to a selected kernel in kernel regressors; and prove that the variance of a learning result is reduced when we adopt a kernel corresponding to a larger reproducing kernel Hilbert space. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICASSP.2012.6288300 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
Hilbert spaces,learning (artificial intelligence),regression analysis,signal processing,information science,kernel machines,kernel regressors,learning,model selection,model space generalization error,nested reproducing kernel Hilbert spaces,signal processing,variance analysis,generalization error,kernel machines,model selection,orthogonal projection,variance | Mathematical optimization,Pattern recognition,Radial basis function kernel,Kernel embedding of distributions,Kernel principal component analysis,Tree kernel,Polynomial kernel,Artificial intelligence,String kernel,Variable kernel density estimation,Mathematics,Kernel (statistics) | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4673-0044-5 | 978-1-4673-0044-5 | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akira Tanaka | 1 | 38 | 12.20 |
| Hideyuki Imai | 2 | 103 | 25.08 |
| Koji Takamiya | 3 | 0 | 0.34 |