The certainty factor-based neural network in continuous classification domains

The integration of certainty factors (CFs) into the neural computing framew ork has resulted in a special artificial neural network known as the CFNet. This paper presents the cont-CFNet, which is devoted to classification dom ains where instances are described by continuous attributes. A new mathemat ical analysis on Learning behavior, specifically linear versus nonlinear le arning, is provided that can serve to explain how the cont-CFNet discovers patterns and estimates output probabilities. Its advantages in performance and speed have been demonstrated in empirical studies.