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.
... set of algorithms. These algorithms are intelligent in the sense that each of them ar- ticulates well what the patterns are, and, by applying these algorithms to data, we inquire of the existence of such kinds of patterns. New ...
... data can be misleading. While neural scientists, over the last decade, have been trying hard to make us know more ... set very large is included in the fuzzy set large. In the other case, it is not; large and very large denote two ...
... dataset into a more economical space by getting rid of these redundancies. So, on the one hand, we want to have a reduced space that is as small as possible; on the other hand, we still want to keep the original information. These two ...
... dataset related to employees' withdrawals from a national pension fund in Brazil. It shows how the work on the principal components analysis can be facilitated by the use of artificial neural networks. This chapter provides a good ...
... set of k cluster centroids. The positions of these centroids are then adjusted iteratively by first as- signing the data samples to the nearest clusters and then recomputing the centroids. The details can be found in Chap. 7, Trading ...
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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