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.
... 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 ...
... regression models) or fuzzy decision rules may be read as a way of coping with this reality, and there may be a neural foundation for fuzzy logic, which is yet to be established. Alternatively, we may ask: is the brain fuzzy, and in ...
... Regression Models, written by Hongxing He, Jie Chen, Huidong Jin, and Shu-Heng Chen. Corresponding to (22), the cost function associated with SOM can be roughly treated as follows12 CSOM = n∑ d(Xmi,Mj) · hw(Xmi),j, (24) i=1 k∑ j=1 ...
... regression or time series forecasting. For example, if we are interested in not xt+1 itself, but in whether xt+1 will be greater than xt, i.e., whether xt will go up or go down, then some regions of the instance space may be very stable ...
Esat sasniedzis šīs grāmatas aplūkošanas reižu limitu.
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 |