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In this paper, we present a novel approach for Remaining Useful Life (RUL) estimation problem in prognostics using a proposed ‘sequential learning Meta-cognitive Regression Neural Network (McRNN) algorithm for function approximation’. The McRNN has two components, namely, a cognitive component and a meta-cognitive components. The cognitive component is an evolving single hidden layer Radial Basis...
Traditional online learning algorithms are designed for vector data only, which assume that the labels of all the training examples are provided. In this paper, we study graph classification where only limited nodes are chosen for labelling by selective sampling. Particularly, we first adapt a spectral-based graph regularization technique to derive a novel online learning linear algorithm which can...
Cost-Sensitive Online Classification is recently proposed to directly online optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. However, the previous existing learning algorithms only utilized the first order information of the data stream. This is insufficient, as recent studies...
Quantitative structure-activity relationship (QSAR) models between tumor necrosis factor a (TNF-a) inhibition activity of lenalidomide analogues and their chemical structures were established by stepwise multiple linear regression (MLR) and support vector machines (SVM) methods. The molecular descriptors of compounds were calculated based on DFT (density functional theory) method, with the basis set...
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