General Noise SVRs and Uncertainty Intervals

Prada, J.; Dorronsoro, José R.

doi:10.1007/978-3-319-59147-6_62

Cited by 1 publication

(3 citation statements)

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“…Full computations to obtain these optimal loss functions are given in our previous work [21]. Note that, technically, at w i ¼ 0 the derivatives corresponding to Laplace distributions are non-differentiable.…”

Section: ) Weibullmentioning

confidence: 99%

“…In [21] we gave the computational steps required for parameter estimation via MLE for all the distributions considered in this work. Newton-Raphson method is used for the Beta, Weibull, and MOGE computations.…”

Section: Parameter Estimationmentioning

confidence: 99%

“…For general noise SVR models using loss functions other than ILF, the density parameters are selected applying the MLE formulas shown in [21], which in some cases involve solving numerically the equations over a set of residuals. We use for this purpose the validation residuals of the previously computed optimum -insensitive SVR.…”

Section: Model Parameters Selectionmentioning

confidence: 99%

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General noise support vector regression with non-constant uncertainty intervals for solar radiation prediction

Prada

Dorronsoro

2018

J. Mod. Power Syst. Clean Energy

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General noise cost functions have been recently proposed for support vector regression (SVR). When applied to tasks whose underlying noise distribution is similar to the one assumed for the cost function, these models should perform better than classical -SVR. On the other hand, uncertainty estimates for SVR have received a somewhat limited attention in the literature until now and still have unaddressed problems. Keeping this in mind, three main goals are addressed here. First, we propose a framework that uses a combination of general noise SVR models with naive online R minimization algorithm (NORMA) as optimization method, and then gives nonconstant error intervals dependent upon input data aided by the use of clustering techniques. We give theoretical details required to implement this framework for Laplace, Gaussian, Beta, Weibull and Marshall-Olkin generalized exponential distributions. Second, we test the proposed framework in two real-world regression problems using data of two public competitions about solar energy. Results show the validity of our models and an improvement over classical -SVR. Finally, in accordance with the principle of reproducible research, we make sure that data and model implementations used for the experiments are easily and publicly accessible.

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Section: ) Weibullmentioning

confidence: 99%

Section: Parameter Estimationmentioning

confidence: 99%

Section: Model Parameters Selectionmentioning

confidence: 99%

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