The certainty factor-based neural network in continuous classification domains.
The integration of certainty factors (CFs) into the neural computing framework has resulted in a special artificial neural network known as the CFNet. This paper presents the cont-CFNet, which is devoted to classification domains where instances are described by continuous attributes. A new mathematical analysis on learning behavior, specifically linear versus nonlinear learning, 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.
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Dept. of Comput. & Inf. Sci., Florida Univ., Gainesville, FL.