In this paper, we propose a classification system based on a multiple-classifier architecture, which is aimed at updating land-cover maps by using multisensor and/or multisource remote-sensing images. The proposed system is composed of an ensemble of classifiers that, once trained in a supervised way on a specific image of a given area, can be retrained in an unsupervised way to classify a new ...

Consider two random variables contaminated by two unknown transformations. The aim of this paper is to test the equality of those transformations. Two cases are distinguished: first, the two random variables have known distributions. Second, they are unknown but observed before contaminations. We propose a nonparametric test statistic based on empirical cumulative distribution functions. Monte ...

In this paper, a partially linear model based on the fused lasso method is proposed to solve problem of high correlation between adjacent variables, and then idea two-stage estimation used study solution model. Firstly, non-parametric part estimated using kernel function transforming semiparametric into parametric Secondly, regularization term introduced construct least squares parameter penalt...

This paper considers the implementation of semiparametric methods in the empirical analysis of consumer demand. The application is to the estimation of the Engel curve relationship and uses the British Family Expenditure Survey. Household composition is modelled using an extended partially linear framework. This is shown to provide a useful method for pooling non-parametric Engel curves across ...