Meanwhile, kernel ridge regression is employed to connect the input low-resolution (LR) image patches with the HR coding coefficients. Experimental results show that the proposed method is effective and efficient in comparison with state-of-art algorithms....
The first strategy is to treat each state separately in a kernel ridge regression model and all states together in a multiclass neural network. The second strategy is to instead encode the state as input into the model, which is tested with both models. Numerical evidence suggests that using ...
In this paper we study multi-task kernel ridge regression and try to understand when the multi-task procedure performs better than the single-task one, in terms of averaged quadratic risk. In order to do so, we compare the risks of the estimators with perfect calibration, the \emph{oracle ...
We analyze this problem in the case of regression and the kernel ridge regression algorithm. We examine the corresponding learn... C Cortes,M Mohri,A Rostamizadeh 被引量: 325发表: 2009年 Composite Kernels for Hypertext Categorisation Kernels are problem-specific functions that act as an interface...
Such a state encoding enables not only kernel ridge regression with multiple outputs but leads also to more accurate machine learning models for state-specific properties. An important goal for excited-state machine learning models is their use in dynamics simulations, which needs not only state-...