Theoretical Analyses on Ensemble and Multiple Kernel Regressors
Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo · Asian Conference on Machine Learning · 2014
For the last few decades, learning based on multiple kernels, such as the ensemble kernel regressor and the multiple kernel regressor, has attracted much attention in the eld of machine learning. Although its ecacy was revealed numerically in many works, its theoretical ground is not investigated suciently. In this paper, we discuss regression problems with a class of kernels whose corresponding reproducing kernel Hilbert spaces have a common subspace with an invariant metric and show that the ensemble kernel regressor (the mean of kernel regressors with those kernels) gives a better learning result than the multiple kernel regressor (the kernel regressor with the sum of those kernels) in terms of the generalization ability of a model space.