Multi-resolution subspace for financial trading

Pattern Recognition Letters

Volumn 27, Issue 2, 2006, Pages 109-115

  Nanni, Loris ^a  

^a UNIVERSITY OF BOLOGNA   (Italy)

Author keywords

Machine learning; Stock trend prediction; Subspace classifier

Indexed keywords

FINANCE; INVENTORY CONTROL; LEARNING SYSTEMS;

FINANCIAL TRADING; MULTI-RESOLUTION SUBSPACE; STOCK TREND PREDICTION; SUBSPACE CLASSIFIER;

PATTERN RECOGNITION;

EID: 27844527990     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2005.07.008     Document Type: Article

References (15)

1. J.C. Bezdek Pattern Recognition with Fuzzy Objective Function Algorithms 1981 Plenum
2. Bhalerao, A., Rajpoot, N., 2003. Discriminant feature selection for texture classification. In Proc. British Machine Vision Conf. (BMVC 2003).
3. Cloete, I., Skabar, A., 2001. Feature selection for financial trading rules. Proc. 13th European Simulation Symposium: Simulation in Industry, 713-717.
4. R.P.W. Duin, and E. Pekalska Possibilities of zero-error recognition by dissimilarity representations Pattern Recognition in Information Systems 2002 20 32
5. R.D. Edwards, and J. Magee Technical Analysis of Stock Trends 2001 Amacom
6. Hellstrm, T., Holmstrm, K., 1998. Predicting the stock market. Technical report series IMa-TOM-1997-07, Malardalen University.
7. Hiemstra, Y., 1994. A stock market forecasting support system based on fuzzy logic. Proc. 27th Annual Hawaii Internat. Conf. on System Sciences, 281-288.
8. L.I. Kuncheva Diversity in multiple classifier systems Information Fusion 6 1 2005 3 4
9. Natsev, A., Smith, J.R., 2002. A study of image retrieval by anchoring. Proc. of IEEE Internat. Conf. on Multimedia and Expo (ICME), 421-424.
10. E. Pekalska, and R.P.W. Duin Dissimilarity representations allow for building good classifiers Pattern Recognition Lett. 23 8 2002 943 956
11. Schierholt, K., Dagli, C.H., 1996. Stock market prediction using different neural network classification architectures. Proc. IEEE/IAFE '96 Conf. on Computational Intelligence for Financial Engineering, 72-78.
12. Tax, D.M.J., 2001. One-class classifiers. Ph.D. thesis.
13. F. Van der Heijden, R.P.W. Duin, D. de Ridder, and D.M.J. Tax Classification, Parameter Estimation and State Estimation: An Engineering Approach using MATLAB 2004 Wiley Hardcover
14. R.J. Van Eyden The Application of Neural Networks in the Forecasting of Share Prices 1996 Finance and Technology Publishing
15. P. Viola, and M. Jones Fast and robust classification using asymmetric AdaBoost and a detector cascade Advances in Neural Information Processing System 14 2002 MIT Press Cambridge