Approximation and Estimation Bounds for Artificial Neural Networks
暂无分享,去创建一个
[1] Robert B. Ash,et al. Information Theory , 2020, The SAGE International Encyclopedia of Mass Media and Society.
[2] C. J. Stone,et al. Optimal Global Rates of Convergence for Nonparametric Regression , 1982 .
[3] Vladimir Vapnik,et al. Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics) , 1982 .
[4] C. J. Stone. OPTIMAL GLOBAL RATES OF CONVERGENCE FOR NONPARAMETRIC ESTIMATORS , 1982 .
[5] J. Rissanen. A UNIVERSAL PRIOR FOR INTEGERS AND ESTIMATION BY MINIMUM DESCRIPTION LENGTH , 1983 .
[6] B. Silverman. Density estimation for statistics and data analysis , 1986 .
[7] M. Nyssbaum. Nonparametric Estimation of a Regression Function that is Smooth in a Domain in $R^k$ , 1987 .
[8] Ker-Chau Li,et al. Asymptotic Optimality for $C_p, C_L$, Cross-Validation and Generalized Cross-Validation: Discrete Index Set , 1987 .
[9] D. Cox. Approximation of Least Squares Regression on Nested Subspaces , 1988 .
[10] M. C. Jones,et al. Spline Smoothing and Nonparametric Regression. , 1989 .
[11] A. Barron,et al. Statistical properties of artificial neural networks , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.
[12] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..
[13] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[14] C. J. Stone,et al. Large-Sample Inference for Log-Spline Models , 1990 .
[15] W. Härdle. Applied Nonparametric Regression , 1991 .
[16] Halbert White,et al. Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings , 1990, Neural Networks.
[17] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[18] A. Barron,et al. APPROXIMATION OF DENSITY FUNCTIONS BY SEQUENCES OF EXPONENTIAL FAMILIES , 1991 .
[19] Andrew R. Barron,et al. Minimum complexity density estimation , 1991, IEEE Trans. Inf. Theory.
[20] L. Jones. A Simple Lemma on Greedy Approximation in Hilbert Space and Convergence Rates for Projection Pursuit Regression and Neural Network Training , 1992 .
[21] David Haussler,et al. Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications , 1992, Inf. Comput..
[22] Andrew R. Barron,et al. Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.