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 71.
... methods that dominated scientific contributions for almost an entire century and continue to dominate early in the twenty-first century. Traditional methods mandate that solutions to problems be based on theoretically defendable ...
... methods when deterministic nonlinear systems were shown to have dynamics that ap- pear random to the untrained eye. The findings rendered linear processes as a unique case in a huge spectrum of possible deterministic systems. Analyzing ...
... methods and implementation of computational intelligence techniques. Such comparative analysis is not to establish the superiority of one over the other. It is rather because today's environment for economic and financial decision ...
... through Statistical and Machine Learning Methods: A Comparison Omar Zambrano, Claudio M. Rocco S, Marco Muselli .................... 93 An Application of Kohonen's SOFM to the Management of Benchmarking Policies Raquel Florez-Lopez ...
... method. In the first stage, the ARIMA model serves as a filter to filter out the linear signal. The residuals are then used to feed the recurrent neural network in the second stage. 6 Some early applications can be found in [35] and [9] ...
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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