Name
Abstract | ||
|---|---|---|
Kernel-based learning is widely known as a powerful tool for various fields of information science such as pattern recognition and regression estimation. For the last few decades, a combination of different learning machines so-called ensemble learning, which includes learning with multiple kernels, have attracted much attention in this field. Although its efficacy was revealed numerically in many works, its theoretical grounds are not investigated sufficiently. In this paper, we discuss regression problems with a class of kernels and show that the generalization error by an ensemble kernel regressor with the class of kernels is smaller than the averaged generalization error by kernel regressors with each kernel in the class. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-662-44415-3_28 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
kernel regressor,ensemble learning,orthogonal projection,generalization error | Radial basis function kernel,Kernel embedding of distributions,Tree kernel,Polynomial kernel,Artificial intelligence,Kernel method,Variable kernel density estimation,Ensemble learning,Mathematics,Machine learning,Kernel (statistics) | Conference |
Volume | ISSN | Citations |
8621 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akira Tanaka | 1 | 38 | 12.20 |
| Ichigaku Takigawa | 2 | 209 | 18.15 |
| Hideyuki Imai | 3 | 103 | 25.08 |
| Mineichi Kudo | 4 | 927 | 116.09 |