Computational Intelligence in Economics and Finance: Volume II, 2. sējumsPaul P. Wang, Tzu-Wen Kuo Springer Science & Business Media, 2007. gada 11. jūl. - 228 lappuses Computational intelligence (CI), as an alternative to statistical and econometric approaches, has been applied to a wide range of economics and finance problems in recent years, for example to price forecasting and market efficiency. This book contains research ranging from applications in financial markets and business administration to various economics problems. Not only are empirical studies utilizing various CI algorithms presented, but so also are theoretical models based on computational methods. In addition to direct applications of computational intelligence, readers can also observe how these methods are combined with conventional analytical methods such as statistical and econometric models to yield preferred results. Chen, Wang, and Kuo have grouped the 12 contributions following their introductory chapter into applications of fuzzy logic, neural networks (including self-organizing maps and support vector machines), and evolutionary computation. All chapters were selected either by invitation or based on a careful selection and extension of best papers from the International Workshop on Computational Intelligence in Economics and Finance in 2005. Overall, the book offers researchers an excellent overview of current advances and applications of computational intelligence techniques to economics and finance problems. |
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1.–5. rezultāts no 60.
... decision-making. Swiftly advancing computer hardware and software technologies nourished their evolution. It was only during the last decade of the century when disciplines such as Computational Economics, Computational Finance, and ...
... decision-making. Introducing advances in computational intelligence thought to practitioners as well as academicians ... decisions to ascertain that significant research contributions reach their audiences and users in a timely manner ...
... decision making is fundamentally different from that of yesterday. Our world is getting more complex and our scientific solutions in decision making problems must advance to accommodate increased complexities and accommodate ...
... decisions to the observed external environment which is frequently characterized by a large number of features. In this way, a formal decision rule can be explicitly generated.2 However, humans are not always confident about what they ...
... decisions in dealing with various risky real-life events, such as earthquake damage, health forecasts, etc. 4. Artificial. Neural. Networks. Among all the economic and financial applications, the artificial neural network (ANN) is probably ...
Saturs
1 | |
An Overview of Insurance Uses of Fuzzy Logic | 24 |
ArnoldF Shapiro 25 | 63 |
Estimating Female Labor Force Participation through Statistical | 93 |
An Application of Kohonens SOFM to the Management | 106 |
Trading Strategies Based on Kmeans Clustering and Regression Models | 123 |
Application of an Instance Based Learning Algorithm for Predicting | 144 |
Nonlinear GoalDirected CPPI Strategy | 183 |
A LogicalHeuristic Approach | 209 |
Index | 224 |
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Computational Intelligence in Economics and Finance: Volume II Paul P. Wang,Tzu-Wen Kuo Priekšskatījums nav pieejams - 2010 |
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