Learning by negation of the negative examples

摘要

The paper presents two different approaches to learning by negation of the negative examples. The first approach is building a single model to represent the positive examples, as the examples not covered by the model are assumed negative. The second approach is building a double model to represent separately positive examples and negative examples. These two approaches are analysed and compared by their pros and cons.