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 18.
... 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 ...
... K-means is related to self-organizing maps and K-nearest neighbors, and how instance-based learning is related to K-nearest neighbors. In this way, we make an effort to make everything as tight as possible and leave the audience with a ...
... the interested reader can find some useful references directly from the website of the SVM: http://www.svms.org 4.4 Self-Organizing Maps and K-Means The genetic programming approach to. CIEF: Shifting the Research Frontier 11.
... K-Means The genetic programming approach to pattern discovery, as mentioned in Sect. 5.2 below, is a symbolic approach. This approach can also be carried out in different ways by other CI tools, such as decision trees.9 The symbolic ...
... K-means Clustering and 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) ...
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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