Email filtering

This bachelor‘s thesis characterizes spam. Spam’s scale and legality are evaluated, and a line is drawn, between eligible and trash emails. Also, email filters are examined by technical aspects. Thesis discusses most common email filter types, introduces their operating principles. Deep look for graphic filters is given, by illustrating picture content recognition principles. Furthermore, Naïve Baye’s filtering theorem is examined comprehensively. In second chapter of this work, classificator, based on Naïve Baye’s algorithm and machine learning principles, is presented. Main ideas and possible applications are given for this classificator. Every component and operating principle is thoroughly described. Finally, email filtering application, based on previously mentioned classificator is briefly discussed, in order to demonstrate it’s capabilities. This application’s prototype, user interface and used baseline data are overviewed