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Ridge Regression using Artificial Neural Network
In this paper, a new suggested method using Ridge Neural Network (RNN) is presented to improve estimation based on using Ridge Regression method (RR). We compared between the proposed method and the existing back propagation algorithm of Artificial Neural Network (ANNs) by using Mean Square Error (MSE). The approach has been simulated using MATLAB. The results showed that the suggested method has the good performance in the sense that RNN method gives less error.
Artificial Neural Network, Back Propagation, Estimation, MATLAB, Mean Square Error, Ridge Regression, Simulation.
- Trentin E, Freno A. Unsupervised nonparametric density estimation. Proceedings of International Joint Conference on Neural Networks; USA. 2009.
- Girosi F. An equivalence between sparce approximations and support vector machines. USA: Massachusetts Institute of Technology; 1997.
- Kimeldorf G, Wahba G. Some results on tchebycheffian spline functions. Jounral of Mathematical Anylasis and Applications,USA. 1971; 33(1):82–95.
- Kriesel D. A brief introduction to neural networks. Bonn, Germany; 2005. Available from: http://www.dkriesel.com/en/science/neural_networks
- Fyfe C. Artificial Neural Network. USA: The University of Paisley; 1996.
- Essai MTE, Mohammed H. From robust statistics to artificial intelligence: M-estimators for training feed-forward neural network. Al-Azhar Engineering 9th International Conference; Assiut University, Egypt. 2007.
- Thomas M. Approximation of complex nonlinear functions by means of neural networks. Germany: Institute of Structural Mechanics, Bauhaus-University Weimar; 2005.
- Freeman JA, Skapura DM. Neural networks,algorithms, applications,and programming techniques. USA: University of Houston; 1991.
- Rashwan NI, El-Dereny M. Solving multicollinearity problem using ridge regression models. Int J Contemp Math Sciences. 2011; 6(12):585.
- Hoerl E, Kennard RW. Ridge regression: Applications to nonorthogonal problems. Technometrics, American Statistical Association, JSOR, USA. 1970; 12(1):69–82.
- Liano K. Robust error measure for supervised neural network learning with outliers. IEEE Trans Neural Networks,. 1996; 7:246–50.
- Drucker H, Burges C, Kaufman L, Smola A, Vapnik VN. Support vector regression machines. Bell Labs and Monmouth University; 1996.
- Saunders C, Gammerman A, Vovk V. Ridge regression learning algorithm in dual variables. Proceedings of the 15th International Conference on Machine Learning; USA. 1998.
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