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The maxi-min margin machine (M4) algorithm, contrast to the traditional support vector machine (SVM) algorithm, gives a more robust solution and gets better generalization performance. In this paper we extend the M4 classification algorithm to deal with regression problem, and propose a novel regression method. This method inherits the characteristics of M4 such as good robustness and generalization...
We evaluate Gaussian Processes (GPs) for the estimation of biophysical parameters from acquired multispectral data. The standard GP formulation is used, and all hyperparameters (kernel parameters and noise variance) are optimized by maximizing the marginal likelihood. This gives rise to a fully-adaptive GP to data characteristics, both in terms of signal and noise properties. The good numerical results...
We propose a simple formulation for regularized kernel function approximation. The regression function is obtained by minimizing an unconstrained quadratic function. Further reduced kernel technique is also employed in its formulation which enables it to handle large database. The solution of this objective function is obtained by solving a system of linear equations and thus no need for any quadratic...
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