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 59.
... Reference [11] (p. 123) details the historical origin of the CIEF. Intellectually, the CIEF carries on the legacy of Herbert Simon, who broke down the conventional distinctions among economics, computer science and cognitive psychology ...
... Reference [39] indicates some directions of the financial applications of the nonlinear PCA. In this volume, Chap. 4 ... references directly from the website of the SVM: http://www.svms.org 4.4 Self-Organizing Maps and K-Means The ...
... Reference [14], in the first volume, is the first one to give this idea a test. They used self-organizing maps to first cluster the windowed time series of the stock index into different clusters, by using the historical data to learn ...
... Reference [3] distinguishes the two by calling the former memory updating functions, and the latter noise-tolerant algorithms. The details can also be found in Chap. 9 of this volume. The addition of a storage-reduction algorithm to KNN ...
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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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Business Applications and Computational Intelligence Kevin E. Voges,Nigel Pope Priekšskatījums nav pieejams - 2006 |