Degradation and cost are the main factors affecting commercial applications of Polymer Electrolyte Membrane Fuel Cells (PEMFC). This paper proposes a novel degradation prediction for PEMFC in various by using Multi-kernel Relevance Vector Regression (MRVR) Whale Optimization Algorithm (WOA). method uses data from vehicle operating under real driving conditions laboratory to derive robust model ...

This paper presents a relevance vector regression (RVR) based parametric approach to the bias field estimation in brain magnetic resonance (MR) image segmentation. Segmentation is a very important and challenging task in brain analysis, while the bias field existed in the images can significantly deteriorate the performance. Most of current parametric bias field correction techniques use a pre-...

support vector regression (svr) solves regression problems based on the concept of support vector machine (svm). in this paper, a new model of svr with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...

In this work we first propose a heteroscedastic generalization to RVM, a fast Bayesian framework for regression, based on some recent similar works. We use variational approximation and expectation propagation to tackle the problem. The work is still under progress and we are examining the results and comparing with the previous works.

The support vector machine (SVM) is a state-of-the-art technique for regression and classification, combining excellent generalisation properties with a sparse kernel representation. However, it does suffer from a number of disadvantages, notably the absence of probabilistic outputs, the requirement to estimate a trade-off parameter and the need to utilise 'Mercer' kernel functions. In this pap...

The Support Vector Machine (SVM) of Vapnik [9] has become widely established as one of the leading approaches to pattern recognition and machine learning. It expresses predictions in terms of a linear combination of kernel functions centred on a subset of the training data, known as support vectors. Despite its widespread success, the SVM suffers from some important limitations, one of the most...