By P. P. M. Pompe, A. J. Feelders (auth.), Phillip Ein-Dor (eds.)
In the prior many years a number of researchers have constructed statistical types for the prediction of company financial ruin, e. g. Altman (1968) and Bilderbeek (1983). A version for predicting company financial disaster goals to explain the relation among financial ruin and a few explanatory monetary ratios. those ratios may be calculated from the data contained in a company's annual document. The is to acquire a style for well timed prediction of financial disaster, a so final objective referred to as "early caution" approach. extra lately, this topic has attracted the eye of researchers within the quarter of desktop studying, e. g. Shaw and Gentry (1990), Fletcher and Goss (1993), and Tam and Kiang (1992). This examine is generally directed on the comparability of desktop studying tools, resembling induction of type timber and neural networks, with the "standard" statistical equipment of linear discriminant research and logistic regression. In past learn, Feelders et al. (1994) played the same comparative research. The tools used have been linear discriminant research, selection bushes and neural networks. We used an information set which contained 139 annual studies of Dutch business and buying and selling businesses. The experiments confirmed that the predicted prediction errors of either the choice tree and neural community have been lower than the predicted mistakes of the linear discriminant. hence apparently we will achieve by way of exchanging the "traditionally" used linear discriminant by way of a extra versatile class approach to expect company financial ruin. the information set utilized in those experiments was once very small however.
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Extra resources for Artificial Intelligence in Economics and Managment: An Edited Proceedings on the Fourth International Workshop: AIEM4 Tel-Aviv, Israel, January 8–10, 1996
4. Send a message to another BLO. It is the third step that contains all the complexity of business logic. Once defining an API to perform steps 1, 2, and 4, we are left with the full power of native Prolog to handle step 3. Perhaps the foremost motivator in the developing three-tier architecture is the well-established difficulty in developing, documenting, and maintaining business logic throughout the system Iifecycle. It is the declarative strength of Prolog that makes it a natural solution for creating self-documenting readily maintainable business logic code.
Knowledge Interchange Format: The KIF of Death," AI Magazine (Fall 1991):57-63,. Kim, W. and F. Lochovsky, Object-Oriented Concepts, Database, and Applications, Reading, MA: Addison-Wesley (1989). G. ", Computer (April 1995). Minker, 1. ), Foundations of Deductive Databases and Logic Programming, Los Altos, CA: Morgan Kaufmann (1988). Neches, R, R Fikes, T. Y. Gruber, R Patil, T. R. Swartout, "Enabling Technology for Knowledge Sharing," AI Magazine (Fall 1991): 37-56. Newell, A. "The Knowledge Level," Artificial Intelligence 18 (1982):87-127.
X h ) - e)} (6) To summarize, in this research we examine, among other parameters, the generalization capability using neural networks as a function of the learning technique of neural network topology. In this study, we use a perceptron like network with functional enhancement (to be defined), and investigate its effect on learning or generalization capabilities. 4. Research Questions and Design of the Experiments This study aims at answering the following questions: What is the relation between the neural network model and the discriminant function D(x)?