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 23.
... agent based modeling, fuzzy logic, wavelets, molecular computing, and other areas of artificial intelligence to solve complex problems. These techniques utilize optimization and computation algorithms impossible to implement without the ...
... agent-based modeling. This book is therefore useful reading for young scholars learning about computational intelligence as well as active researchers and practitioners eager to learn about recent advancements in computational ...
... -Shing Chen, Benjamin Penyang Liao .............................. 183 Hybrid-Agent Organization Modeling: A Logical-Heuristic Approach Ana Marostica, Cesar A. Briano, Ernesto Chinkes ....................... 209 Index ...
... agent-based modeling. Nonetheless, there are also new faces appearing in this volume, including recursive neural networks, self-associative neural networks, Kmeans and instance-based learning. Given the large degree of similarity to ...
... agents are connected to human agents. For more details, see [12]. 3 When a listener hears that x is large, he assumes that x is not very large, because in the latter case the speaker would have used the more informative utterance x is ...
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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