A variant of the WEBSOM architecture for information retrieval is proposed in this paper. WEBSOM is based on the self-organizing map that employs a linear LMS adaptation rule for updating the weight vector of each neuron. Accordingly, the weight vector converges asymptotically to the conditional cluster mean of the feature vectors assigned to the class represented by the weight vector of the ne...

No doubt, the performance of a Content-Based Image Retrieval (CBIR) system depends on a) how efficient the image visual content is represented and b) the degree of importance, which is assigned to each content-descriptor. In the first case, efficient visual representation is achieved, apart from the extraction of appropriate descriptors, through a proper organization of them [1]. The second cas...

In this study, the finite element model updating was simulated by reducing the stiffness of the members. Due to lack of access to the experimental results, the data obtained from an analytical model were used in the proposed structural damage scenarios. The updating parameters for the studied structures were defined as a reduction coefficient applied to the stiffness of the members. Parameter v...

We consider the problem of semiparametric Bayes joint modeling of predictors and a response variable, with a particular emphasis on functional predictors. Parametric models for the predictor and response are coupled through a joint distribution for subjectspecific predictor and response coefficients. This joint distribution is assigned a flexible mixture prior, which allows the response distrib...

This paper provides experimental evidence on how players predict end-game effects in a linear public good game. Our regression analysis yields a measure of the relative importance of priors and signals on subjects’ beliefs on contributions and allows us to conclude that, first, the weight of the signal is relatively unimportant, while priors have a large weight and, second, priors are the same ...

We derive global H 1 optimal training algorithms for neural networks. These algorithms guarantee the smallest possible prediction error energy over all possible disturbances of xed energy, and are therefore robust with respect to model uncertainties and lack of statistical information on the exogenous signals. The ensuing es-timators are innnite-dimensional, in the sense that updating the weigh...