论文信息 - Interval Arithmetic Multilayer Perceptron as Possibility-Necessity Pattern Classifier

Interval Arithmetic Multilayer Perceptron as Possibility-Necessity Pattern Classifier

In the work presented in this paper an Interval Arithmetic MLP (IAMLP) is used to detect the region in the input space to which an uncertainty decision should be appropriately associated. This region may be originated both by sub-regions which are not represented in the training set and by sub-regions where the probabilities of the two classes are very similar. To train the IAMLP, an algorithm will be presented which in particular is able detect the two certainty regions and the uncertainty one. The algorithm has been used for studying a simple artificial problem and one real-world application, the Breast Cancer data base.

Sandro Ridella | Gian Paolo Drago

[1] Norio Baba,et al. A new approach for finding the global minimum of error function of neural networks , 1989, Neural Networks.

[2] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[3] Sandro Ridella,et al. Possibility and Necessity Pattern Classification using an Interval Arithmetic Perceptron , 1999, Neural Computing & Applications.

[4] Sandro Ridella,et al. Pruning with interval arithmetic perceptron , 1998, Neurocomputing.

[5] Sandro Ridella,et al. Statistically controlled activation weight initialization (SCAWI) , 1992, IEEE Trans. Neural Networks.

[6] Hisao Ishibuchi,et al. Possibility and necessity pattern classification using neural networks , 1992 .