| Expert system (8) |  |
| Machine intelligence (2) | |
| Machine learning (35) | |
| Neural network (19) | |
| Expert system (8) | |
| Machine intelligence (2) | |
| Machine learning (35) | |
| Neural network (19) | |
Other techniques of knowledge discovery, such as neural networks, genetic algorithms, induction or other multistrategy machine learning hybrid tools PIA 91
which included Z-Scores, ZETA Scores, and Neural networks (NN). The strengths and weaknesses of each model were exposed
and Neural networks are examples of models that relate to internal factors. Utilizing SMES indiscriminately will negatively affect the outcome of the majority of SME studies.
such as the ZETA and Neural networks models, require a high level of information intensity. That implies the need for detailed data,
Examples for such models are the ZETA model, the Neural networks model, and the SIV model.
The other group includes Z-Scores, ZETA Scores, Neural networks, and the SIV model. These are more suitable to the investigation of firm performance in relation to the internal environment of an enterprise.
such as the ZETA and Neural networks models, require a high information intensity level. Such a requirement can be a problem
Comparisons using linear discriminant analysis and neural networks (the Italian experience. Journal of Banking and Finance 18 (3), 505 529.
Neural networks versus logistic regression in predicting bank failure. In R. P. Srivastava (ed.)Auditing Symposium. Vol:
Data mining with neural networks: Solving business problems from application development to decision support. Mcgraw-hill, Inc. Hightstown, New jersey, USA.
Neural networks and the mathematics of chaos an investigation of these methodologies as accurate predictors of corporate bankruptcy.
Generalization with neural networks. Decision Support systems 11 (5), 527 545. Edvinsson, L. and Malone, M. S. 1997.
Forecasting small air carrier bankruptcies using a neural network approach. Journal of Financial Management and Analysis 13 (19), 44 49.
Performance evaluation of neural network decision models. Journal of Management Information systems 14 (2), 201 216. Jaques, E. 1951.
An empirical investigation of some data effects on the classification accuracy of probit, ID3 and neural networks.
Trading equity index futures with a neural network: A machine learning-enhanced trading strategy. The Journal of Portfolio Management 19 (1), 27 33.
Popular techniques related to storing and enforcing high-level information include neural networks, expert systems, statistical association, conditional probability distributions, different kinds of monotonic and nonmonotonic, fuzzy logic, decision trees, static and dynamic
Neural networks are employed in this approach to classify features extracted from video blobs for their classification task.
IEEE Transactions on Neural networks 13 (4), 793 810 (2002) 18. Qian, R.,Haering, N.,Sezan, I.:
< Back - Next >
Overtext Web Module V3.0 Alpha
Copyright Semantic-Knowledge, 1994-2011