Abstract
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.
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References
Vapnik V.N., The Nature of Statistical Learning Theory, New York: Springer-Verlag, 1995.
Ishibuchi H., Fujioka, Tanaka H., Possibility and necessity pattern classification using neural networks, Fuzzy Nets and Systems, 48, 1992, pp 331–340.
Drago G.P. and Ridella S., Possibility and Necessity Pattern Classification using an Interval Arithmetic Perceptron, accepted for publication on Neural Computing & Applications.
G.P. Drago, S. Ridella, Statistically Controlled Activation Weight Initialization, IEEE Transactions on Neural Networks, Vol.3, no. 4, July 1992, pp 627–631.
Drago G.P. and Ridella S., An Adaptive Momentum Back Propagation (AMBP), Neural Computing & Applications, no.3, 1995, pp.213–221.
Baba N., A new approach for founding the global minimum of error function of neural networks, Neural Networks, Vol.2, pp 367–373, 1989.
Drago G.P. and Ridella S., Pruning with Interval Arithmetic Perceptron, Neurocomputing, Vol.18, no.1–3, 1998, pp. 229–246.
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© 1999 Springer-Verlag London Limited
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Drago, G.P., Ridella, S. (1999). Interval Arithmetic Multilayer Perceptron as Possibility-Necessity Pattern Classifier. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets WIRN Vietri-99. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0877-1_6
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DOI: https://doi.org/10.1007/978-1-4471-0877-1_6
Publisher Name: Springer, London
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