Support Vector Machine Regression for Volatile Stock Market Prediction

Author:

Yang Haiqin,Chan Laiwan,King Irwin

Publisher

Springer Berlin Heidelberg

Reference11 articles.

1. K. R. Müller, A. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen and V. N. Vapnik. Predicting time series with support vector machines. ICANN, 999–1004, 1997.

2. T. B. Trafalis and H. Ince. Support vector machine for regression and applications to financial forecasting. IJCNN2000, 348–353.

3. A. Smola and B. Schölkopf. A Tutorial on Support Vector Regression. 1998, Technical Report NeuroCOLT NC-TR-98-030.

4. V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, New York, 1995.

5. V N. Vapnik, S. Golowich and A. Smola. Support vector method for function approximation, regression estimation and signal processing.

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