Feedforward Neural Nets

The sections in this article are 1 Neural Network Elements and Notation 2 The Literature 3 Roles for Artificial Neural Networks 4 Mathematical Setup for Feedforward Neural Networks 5 Basic Properties of the Representation by Neural Networks 6 The Representational Power of a Single-Hidden-Layer Network 7 Training a Neural Network: Background and Error Surface 8 Training: Backpropagation 9 Descent Algorithms 10 Trends and Open Problems

[1]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[2]  M. Stone Asymptotics for and against cross-validation , 1977 .

[3]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[4]  J. Rissanen Stochastic Complexity and Modeling , 1986 .

[5]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[6]  Bernard Widrow,et al.  Sensitivity of feedforward neural networks to weight errors , 1990, IEEE Trans. Neural Networks.

[7]  Eduardo D. Sontag,et al.  Feedback Stabilization Using Two-Hidden-Layer Nets , 1991, 1991 American Control Conference.

[8]  Isabelle Guyon,et al.  Design of a neural network character recognizer for a touch terminal , 1991, Pattern Recognit..

[9]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[10]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[11]  L. Jones A Simple Lemma on Greedy Approximation in Hilbert Space and Convergence Rates for Projection Pursuit Regression and Neural Network Training , 1992 .

[12]  Roberto Battiti,et al.  First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method , 1992, Neural Computation.

[13]  Andrew R. Barron,et al.  Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.

[14]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[15]  F. Vallet,et al.  Robustness in Multilayer Perceptrons , 1993, Neural Computation.

[16]  Allan Pinkus,et al.  Multilayer Feedforward Networks with a Non-Polynomial Activation Function Can Approximate Any Function , 1991, Neural Networks.

[17]  Alon Orlitsky,et al.  Lower bounds on threshold and related circuits via communication complexity , 1994, IEEE Trans. Inf. Theory.

[18]  C. Fefferman Reconstructing a neural net from its output , 1994 .

[19]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[20]  Barak A. Pearlmutter Fast Exact Multiplication by the Hessian , 1994, Neural Computation.

[21]  Lawrence D. Jackel,et al.  Neural Network Applications in Character Recognition and Document Analysis , 1994 .

[22]  Ali A. Minai,et al.  Perturbation response in feedforward networks , 1994, Neural Networks.

[23]  Halbert White,et al.  Sup-norm approximation bounds for networks through probabilistic methods , 1995, IEEE Trans. Inf. Theory.

[24]  Gavin J. Gibson,et al.  Exact Classification with Two-Layer Neural Nets , 1996, J. Comput. Syst. Sci..