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 55.
... techniques useful in 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 ...
... techniques available and being developed under the umbrella of computational intelligence must ultimately produce more successful and more reliable systems than those offered by traditional methods. The emerging breed of computational ...
... techniques when investigating market efficiency and predicting financial market conditions. Because nonlinear systems characteristically defining the real dynamics of financial markets, use of computational intelligence techniques ...
... Techniques for Learning to Predict Changes in Stock Prices David B. LeRoux ..................................................135 Application of an Instance Based Learning Algorithm for Predicting the. Forecasting Agricultural Commodity ...
... techniques which can be seen in Vol. 1 are not presented here, including rough sets, wavelets, swarm intelligence (ant algorithms), and agent-based modeling. Nonetheless, there are also “new faces” appearing in this volume, including ...
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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