<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bartkowiak, A.</style></author><author><style face="normal" font="default" size="100%">Evelpidou, N.</style></author><author><style face="normal" font="default" size="100%">Vassilopoulos, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Problems encountered when determining the risk of an event in case of imprecise information.</style></title><secondary-title><style face="normal" font="default" size="100%">Symposia on Methods of Artificial Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We consider the problem of predicting a risk index of an event on the base of several predictors. The specificity of the problem lies in the fact, that there are no training data; instead we have a set of logical rules describing in a fuzzy way several classes of the risk (’very low’, ’low’, ’medium’, ’high’ and ’very high’ risk). We present several problems and doubts we had met when trying to construct a predictor. After considering several alternatives we stated that a simple neural network architecture (with one perceptron) calculating logistic regression has given the best agreement with expert opinion. Thus,&lt;br&gt;based on the rules, we are able to mimic the expert opinion by a neural network model.&lt;/p&gt;</style></abstract></record></records></xml>