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. |
No grāmatas satura
1.–5. rezultāts no 24.
... Based on K-means Clustering and Regression Models Hongxing He, Jie Chen, Huidong Jin, Shu-Heng Chen .................... 123 Comparison of Instance-Based Techniques for Learning to Predict Changes in Stock Prices David B. LeRoux ...
Volume II Paul P. Wang, Tzu-Wen Kuo. Application of an Instance Based Learning Algorithm for Predicting the Stock Market Index Ruppa K. Thulasiram, Adenike Y. Bamgbade ............................ 145 Evaluating the Efficiency of Index ...
... 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 Vol. 1 ...
... instance-based learning. K-Means clustering, developed by J.B. MacQueen in 1967 ([37]), is one of the widely used non-hierarchical clustering algorithms that groups data with similar characteristics or features together. K-means and ...
... example, it is almost impossible by using the conventional DEA to see whether efficient firms are uniformly distributed on the efficient frontier, or whether they are grouped into ... Instance-Based Learning In the first. 14 S.-H. Chen et al.
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 |
Citi izdevumi - Skatīt visu
Computational Intelligence in Economics and Finance: Volume II Paul P. Wang,Tzu-Wen Kuo Priekšskatījums nav pieejams - 2010 |
Bieži izmantoti vārdi un frāzes
Populāri fragmenti
Atsauces uz šo grāmatu
Business Applications and Computational Intelligence Kevin E. Voges,Nigel Pope Priekšskatījums nav pieejams - 2006 |