Projection pursuit and the solvability condition applied to constructive learning

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn 2, Issue , 1997, Pages 1062-1067

  Von Zuben, F J ^a   De Andrade Netto, M L ^a  

^a UNIVERSITY OF CAMPINAS   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION PROBLEMS; COMPUTATIONAL LEARNING; CONNECTIONIST MODELS; CONSTRUCTIVE LEARNING; FUNCTION APPROXIMATION; RATES OF CONVERGENCE; SINGLE-HIDDEN-LAYER NEURAL NETWORKS; SOLVABILITY CONDITIONS;

ITERATIVE METHODS; LEARNING ALGORITHMS; NEURAL NETWORKS; APPROXIMATION THEORY; CONVERGENCE OF NUMERICAL METHODS; FUNCTIONS; LEARNING SYSTEMS; MATHEMATICAL MODELS; MATRIX ALGEBRA; OPTIMIZATION; VECTORS;

NEURONS; NEURAL NETWORKS;

COMPUTATIONAL LEARNING ALGORITHMS; SINGLE HIDDEN LAYER NEURAL NETWORKS;

EID: 0030650655     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNN.1997.616175     Document Type: Conference Paper

References (15)

1. F. Barmann, and F. Biegler-Konig, "On a Class of Efficient Learning Algorithms for Neural Networks", Neural Networks, 5: 139-144, 1992.
2. A.R. Barron, "Universal approximation bounds for superpositions of a sigmoidal function", IEEE Transactions on Information Theory, 39: 930-945, 1993.
3. P. Craven, and G. Wahba, "Smoothing Noisy Data with Spline Functions: Estimating the Correct Degree of Smoothing by the Method of Generalized Cross-Validation", Numerische Mathematik, 31: 377-403, 1979.
4. R.A. DeVore, R. Howard, and C. Miccheli, "Optimal Nonlinear Approximation", Manuscripta Mathematica, 63: 469-478, 1989.
5. J.H. Friedman, and W. Stuetzle, "Projection Pursuit Regression", Journal of the American Statistical Association (JASA), 76: 817-823, 1981.
6. F. Girosi, M. Jones, and T. Poggio, "Regularization Theory and Neural Networks Architectures", Neural Computation, 7: 219-269, 1995.
7. R. Hecht-Nielsen, Neurocomputing, Addison-Wesley Publishing Company, 1990.
8. P.J. Huber, "Projection Pursuit (with Discussion)", The Annals of Statistics, 13: 435-475, 1985.
9. J.-N. Hwang, S.-R. Lay, M. Maechler, R.D. Martin, and J. Schimert, "Regression Modeling in Back-Propagation and Projection Pursuit Learning", IEEE Transactions on Neural Networks, 5: 342-353, 1994.
10. I.T. Jolliffe, Principal Component Analysis, New York: Springer-Verlag, 1986.
11. L.K. Jones, "On a conjecture of Huber concerning the convergence of projection pursuit regression", The Annals of Statistics, 15: 880-882, 1987.
12. H.N. Mhaskar, and C.A. Micchelli, "How to Choose an Activation Function", in J.D. Cowan, G. Tesauro, J. Alspector (eds.) Advances in Neural Inform. Processing Systems, Morgan Kaufmann Publishers, 6: 319-326,1994.
13. C.B. Roosen, and T.J. Hastie, "Automatic Smoothing Spline Projection Pursuit". Journal of Computational and Graphical Statistics, 3: 235-248, 1994.
14. Y. Zhao, and C.G. Atkenson, "Implementing Projection Pursuit Learning", IEEE Transactions on Neural Networks, 7: 362-373, 1996.
15. F.J.V. Zuben, and M.L.A. Netto, "Unit-growing learning optimizing the solvability condition for model-free regression", Proceedings of the IEEE International Conference on Neural Networks, 2: 795-800, 1995.