Abstract: Electronic document classification comprising providing training documents sorted into classes; linear programming including selecting inputs which maximize an output, given constraints on inputs, the output maximized being a difference between: a. first estimated probability that a document instance will be correctly classified, by a classifier corresponding to given inputs, as belonging to its own class, and b. second estimated probability that document instance will be classified, by the classifier, as not belonging to its own class; and classifying electronic document instances into classes, using a preferred classifier corresponding, to the inputs selected by the linear programming. A computerized electronic document forgery detection method provides training documents and uses a processor to select value-ranges of non-trivial parameters, such that selected values-range(s) of parameters are typical to an authentic document of given class, and atypical to a forged document of same class.

Abstract: Electronic document classification comprising providing training documents sorted into classes; linear programming including selecting inputs which maximize an output, given constraints on inputs, the output maximized being a difference between: a. first estimated probability that a document instance will be correctly classified, by a classifier corresponding to given inputs, as belonging to its own class, and b. second estimated probability that document instance will be classified, by the classifier, as not belonging to its own class; and classifying electronic document instances into classes, using a preferred classifier corresponding, to the inputs selected by the linear programming. A computerized electronic document forgery detection method provides training documents and uses a processor to select value-ranges of non-trivial parameters, such that selected values-range(s) of parameters are typical to an authentic document of given class, and atypical to a forged document of same class.