Intelligent systems in accounting, finance and managementassessing predictive performance of ann-based classifiers, страница 25

Lachtermacher G, Fuller JD. 1995. Backpropagation in time series forecasting. Journal of Forecasting 14: 381– 393.

Lehtinen J. 1996. Financial ratios in an international comparison. Acta Wasaensia No. 49, Vasa.

Lönnblad L, Peterson C, Rögnvaldsson T. 1992. Mass reconstruction with a neural network. Physics Letters B 278: 181–186.

Marais ML, Patell JM, Wolfson MA. 1984. The experimental design of classification models: aAn application of recursive partitioning and bootstrapping to commercial bank loan classification. Journal of Accounting Research 22: 87–114.

Martín-del-Brío B, Serrano Cinca C. 1993. Self organizing neural networks for the analysis and representation of data: some financial cases. Neural Computing & Applications 1(3): 193–206.

Masters T. 1994. Practical Neural Network Recipes in C++. Academic Press: Boston, MA.

Michalewicz Z. 1992. Genetic Algorithms + Data Structures = Evolution Programs. Springer: Berlin.

Miller JF, Thomson P. 1998. Aspects of digital evolution: geometry and learning. In Proceedings of the 2nd International Conference on Evolvable Systems—ICES98, 23–25 September, EPFL, Lausanne, Switzerland. Moller MF. 1993. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6: 525–533.

Nastac I, Costea A. 2004. A retraining neural network technique for glass manufacturing data forecasting. In Proceedings of 2004 IEEE International Joint Conference on Neural Networks (IJCNN 2004), Vol. 4, Track: Time Series Analysis, IEEE, Budapest, Hungary, 25–29 July; 2753–2758.

Nastac I, Koskivaara E. 2003. A Neural Network Model for Prediction: Architecture and Training Analysis. TUCS Technical Report No. 521. Turku Centre for Computer Science.

Ohlsson M, Peterson C, Pi H, Rögnvaldsson T, Söderberg B. 1994. Predicting utility loads with artificial neural networks—methods and results from the great energy predictor shootout. In Annual Proceedings of ASHRAE.

O’Leary DE. 1998. Using neural networks to predict corporate failure. International Journal of Intelligent Systems in Accounting, Finance and Management 7(3): 187–197.

Pendharkar PC. 2002. A computational study on the performance of artificial neural networks under changing structural design and data distribution. European Journal of Operational Research 138(1): 155–177.

Pendharkar PC, Rodger JA. 2004. An empirical study of impact of crossover operators on the performance of non-binary genetic algorithm based neural approaches for classification. Computers & Operations Research 31(4): 481–498.

Quinlan JR. 1993a. A case study in machine learning. In Proceedings of ACSC-16 Sixteenth Australian Computer Science Conference, Brisbane, January; 731–737.

Quinlan JR. 1993b. C4.5 Programs for Machine Learning. Morgan Kaufmann Series in Machine Learning. Morgan Kaufmann Publishers: San Mateo.

Rogero JM. 2002. A genetic algorithms based optimisation tool for the preliminary design of gas turbine combustors. PhD thesis, Cranfield University.

Rudolfer S, Paliouras G, Peers I. 1999. A comparison of logistic regression to decision tree induction in the diagnosis of carpal tunnel syndrome. Computers and Biomedical Research 32(5): 391–414.

Rudolph G. 1994. Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks 5(1): 96–101.

Schaffer JD. 1994. Combinations of genetic algorithms with neural networks or fuzzy systems. In Computational Intelligence: Imitating Life, Zurada JM, Marks RJ, Robinson CJ (eds). IEEE Press: New York; 371– 382.

Schaffer JD, Whitley D, Eshelman LJ. 1992. Combinations of genetic algorithms and neural networks: a survey of the state of the art. In COGANN-92 Combinations of Genetic Algorithms and Neural Networks. IEEE Computer Society Press: Los Alamitos, CA; 1–37.