Using a Bayesian NeuralNetwork as a Tool for DocumentFiltering Considering User Profiles

Abstract: This thesis describes methods and problems when using Bayesian Ar- tificial Neural Networks for text document classification. It also depict other methods used in text analysis and automated classification in gen- eral. The main tasks are to construct a network, investigate the effect of variations to existing parameters and how to combine dependent input attributes into complex columns. Correlation measures are used to find these combinations. The basic idea is to let the classifier built work as a document filtering system. Results from the testing are described and explained. The results are discouraging. All tests indicate that the training set is too small. Compared to another study done, on the same data, at Swedish Institute of Computer Science the performance of the classifier is poor.