See details in the user manual. The except sub-network, on the other hand, is learned using except labeled examples as positive and accept examples as negative. The learning program learns a specific architecture of linear separators. For Winnow and Perceptron you can choose to cycle through the training data a few times. Read the documentation that comes with the program.
Comments are welcome.
Given a training corpus you will use FEX to generate a lexicon of features and a set of SNoW examples. The first deliverable is a FEX Script and the corresponding Lexicon file - a list of all features and their index. As a matter of convention, the targets are also considered features and FEX will give the indices 0 and 1. In your work, you will train your classifier on the given training data and test it on a separate set of examples, that will be given to you later. It is important that this split is defined before you run FEX on the data.