By Augustin T., Wolff J.

Retrospectively accrued period information are usually stated incorrectly. a huge kind of such an mistakes is heaping - respondents are inclined to round-off or round-up the information in line with a few rule of thumb. for 2 targeted circumstances of the Weibull version we examine the behaviour of the 'naive estimators', which easily forget about the dimension blunders because of heaping, and derive closed expressions for the asymptotic bias. those effects supply a proper justification of empirical facts and simulation-based findings stated within the literature. also, events the place a striking bias should be anticipated should be pointed out, and an actual bias correction might be played.

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The training examples in RBFs), and the invisible/unknown parameters can be estimated through harmony learning between these two domains. Chen et al. (1991) proposed orthogonal least square (OLS) learning to determine the optimal centers. The OLS combines the orthogonal transform with the forward regression procedure to select model terms from a large candidate term set. The advantage of employing an orthogonal transform is that the responses of the hidden layer neurons are decorrelated so that the contribution of individual candidate neurons to the approximation error reduction can be evaluated independently.

The next section introduces the neural network encoding method, and in Sect. 2 the computation of the inductive bias is given. In Sect. 3 we derive the sensitivity measures, and in Sect. 4 we demonstrate the experimental results. The outcomes and conclusions are discussed in Sect. 5. 1 © IEEE. , Yeung, D. S. and Wang, X. (2004). Sensitivity analysis of prior knowledge in knowledge-based neurocomputing. In Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 7:4174–4181.

All the F (x), fθ (x) and R∗SM are vectors and thus the sum of R∗SM values of all the K RBFNN outputs are minimized in the MC2 SG. One may notice that the minimization of the sum of the R∗SM values of all the outputs is equivalent to the minimization of the average of them. However, the average of the R∗SM values may provide a better interpretation and its range is not affected by the value K. The determination of the constant a is made according to the classifier’s output schemes for classification.

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A bias analysis of Weibull models under heaped data by Augustin T., Wolff J.

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