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In this letter, we explore the effectiveness of a novel regression method in the context of the estimation of biophysical parameters from remotely sensed imagery as an alternative to state-of-the-art regression methods like those based on artificial neural networks and support vector machines. This method, called Gaussian process (GP) regression, formulates the learning of the regressor within a Bayesian...
In this paper, we propose a kernel hat matrix based learning stage for outlier removal. In particular, we show that the Gaussian kernel hat matrix have very interesting discriminative properties under the condition of choosing appropriate values for kernel parameters. Thus, we develop a practical model selection criteria in order to well separate the ldquooutlierrdquo distribution from the ldquodominantrdquo...
Although supervised learning has been widely used to tackle problems of function approximation and regression estimation, prior knowledge fails to be incorporated into the data-driven approach because the form of input-output data pairs are not applied. To overcome this limitation, focusing on the fusion between rough fuzzy system and very rare samples of input-output pairs with noise, this paper...
Kernel ridge regression (KRR) is a nonlinear extension of the ridge regression. The performance of the KRR depends on its hyperparameters such as a penalty factor C, and RBF kernel parameter sigma. We employ a method called MCV-KRR which optimizes the KRR hyperparameters so that a cross-validation error is minimized. This method becomes equivalent to a predictive approach to Gaussian process. Since...
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