Model selection via worst-case criterion for nonlinear bounded-error estimation

Model selection via worst-case criterion for nonlinear bounded-error estimation

Set inversion via interval analysis for nonlinear bounded-error estimation

AlWlmet--ln the context of bounded-error estimation, one is interested in characterizing the set of all the values of the parameters to be estimated that are consistent with the data in the sense that the errors between the data and model outputs fall within prior bounds. While the problem can be considered as solved when the model output is linear in the parameters, the situation is far less a...

A Fast Iterative Solution for Worst-case Parameter Estimation with Bounded Model Uncertainties

This paper deals with the problem of worst-case parameter estimation in the presence of bounded uncertainties in a linear regression model. The problem has been formulated and solved in [1,2]. It distinguishes itself from other estimation schemes, such as total-least-squares and H, methods, in that it explicitly incorporates an a-priori bound on the size of the Uncertainties. The closed-form so...

Error estimation and model selection

Machine learning algorithms search a space of possible hypotheses and estimate the error of each hypotheses using a sample. Most often, the goal of classification tasks is to find a hypothesis with a low true (or generalization) misclassification probability (or error rate); however, only the sample (or empirical) error rate can actually be measured and minimized. The true error rate of the ret...

Nonlinear bounded-error state estimation of continuous-time systems