Incorporating functional knowledge in neural networks

Journal of Machine Learning Research

Volumn 10, Issue , 2009, Pages 1239-1262

  Dugas, Charles ^a   Bengio, Yoshua ^a   Bélisle, François ^b   Nadeau, Claude ^b   Garcia, René ^c  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

^b HEALTH CANADA   (Canada)

^c CIRANO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

APRIORI; FUNCTION CLASS; GENERALIZATION PERFORMANCE; INCORPORATING PRIOR KNOWLEDGE; LIPSCHITZ; MONOTONICITY; NEW CLASS; UNIVERSAL APPROXIMATION; UNIVERSAL APPROXIMATOR;

NEURAL NETWORKS;

LEARNING ALGORITHMS;

EID: 67650471279     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (11)

1. A. R. Barron. Universal approximation bounds for superpositions of a sigmoidal function. IEEE Transactions on Information Theory, 39(3):930-945, 1993.
2. F. Black and M. Scholes. The pricing of options and corporate liabilities. Journal of Political Economy, 81(3):637-654, 1973.
3. G. Cybenko. Continuous valued neural networks with two hidden layers are sufficient. Technical report, Department of Computer Science, Tufts University, Medford, MA, 1988.
4. G. Cybenko. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 2:303-314, 1989.
5. M.C. Delfour and J.-P. Zolésio. Shapes and Geometries: Analysis, Differential Calculus, and Optimization. SIAM, 2001.
6. C. Dugas, O. Bardou, and Y. Bengio. Analyses empiriques sur des transactions d'options. Technical Report 1176, Department d'informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Québec, Canada, 2000.
7. R. Garcia and R. Gençay. Pricing and hedging derivative securities with neural networks and a homogeneity hint. Technical Report 98s-35, CIRANO, Montréal, Québec, Canada, 1998.
8. K. Hornik, M. Stinchcombe, and H. White. Multilayer feedforward networks are universal approximators. Neural Networks, 2:359-366, 1989.
9. J.M. Hutchinson, A.W. Lo, and T. Poggio. A nonparametric approach to pricing and hedging derivative securities via learning networks. Journal of Finance, 49(3):851-889, 1994.
10. M. Leshno, V. Lin, A. Pinkus, and S. Schocken. Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Networks, 6:861-867, 1993.
11. J. Moody. Prediction Risk and Architecture Selection for Neural Networks. Springer, 1994.