A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf
.
A Relationship Between Generalization Error and Training Samples in Kernel Regressors
2010
2010 20th International Conference on Pattern Recognition
A relationship between generalization error and training samples in kernel regressors is discussed in this paper. The generalization error can be decomposed into two components. One is a distance between an unknown true function and an adopted model space. The other is a distance between an estimated function and the orthogonal projection of the unknown true function onto the model space. In our previous work, we gave a framework to evaluate the first component. In this paper, we theoretically
doi:10.1109/icpr.2010.351
dblp:conf/icpr/TanakaIKM10
fatcat:u7eg2dcubrge5ddc7muwulhfie