We show that the Bayesian evidence framework can be applied to both ϵ-support vector regression (ϵ-SVR) and ν-support vector regression (ν-SVR) algorithms.
In this paper, Bayesian support vector regression (SVR) model is developed for structural reliability analysis adaptively. Two SVR models, namely, ...
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BSVR provides error bars along with the predicted traffic states. We perform sensitivity and specificity analysis to evaluate the efficiency of BSVR in ...
Mar 9, 2017 · Support vector regression (SVR) has long been proven to be a successful tool to predict financial time series. The core idea of this study ...
In this paper, Bayesian support vector regression (SVR) model is developed for structural reliability analysis adaptively. Two SVR models, namely, least-square ...
Aug 30, 2021 · This paper aims to develop a strategy for solving RBDO problems by support vector regression (SVR) under the Bayesian inference, referred to as ...
In this paper, we consider two popular techniques for ap- plying SVMs to the regression problems, namely the. -support vector regression ( -SVR) [1] and the -.
A new Bayesian formulation is developed for nonlinear support vector machines. (SVMs), based on a Gaussian process and with the SVM hinge loss expressed as.
Dec 1, 2019 · Support vector machine (SVM) is a popular classification method for the analysis of wide range of data including big data.
I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori. (MAP) solutions to inference problems with Gaussian Process ...