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1.
Int J Neural Syst ; 21(4): 311-7, 2011 Aug.
Artículo en Inglés | MEDLINE | ID: mdl-21809477

RESUMEN

The assessment of the risk of default on credit is important for financial institutions. Different Artificial Neural Networks (ANN) have been suggested to tackle the credit scoring problem, however, the obtained error rates are often high. In the search for the best ANN algorithm for credit scoring, this paper contributes with the application of an ANN Training Algorithm inspired by the neurons' biological property of metaplasticity. This algorithm is especially efficient when few patterns of a class are available, or when information inherent to low probability events is crucial for a successful application, as weight updating is overemphasized in the less frequent activations than in the more frequent ones. Two well-known and readily available such as: Australia and German data sets has been used to test the algorithm. The results obtained by AMMLP shown have been superior to state-of-the-art classification algorithms in credit scoring.


Asunto(s)
Algoritmos , Inteligencia Artificial , Redes Neurales de la Computación , Plasticidad Neuronal , Bases de Datos Factuales , Humanos
2.
Talanta ; 33(11): 929-34, 1986 Nov.
Artículo en Inglés | MEDLINE | ID: mdl-18964231

RESUMEN

Mathematical relationships determining the shape of absorbance vs. pH graphs of polybasic acids are derived, and new graphical and numerical techniques for evaluating the acidity constants of two-step overlapping equilibria are examined.

3.
Talanta ; 33(6): 531-5, 1986 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-18964134

RESUMEN

A brief review of the numerical methods employed in the evaluation of acidity constants of two step overlapping equilibria is given. A new alternative to the method of three equations developed by Thamer is also proposed in this paper. Taking into account the Gauss-elimination method, several variants to the original method of Thamer are devised.

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