Structural adaptation and generalization in supervised feed-forward networks

An analytical method that can readily and/or continuously detect ultratrace or minute levels of bischloromethyl ether in air is urgently needed because of its high toxicity. A novel gas chromatographic method or system employing a primary adsorber and two analytical columns gated in sequence is disclosed and claimed. Such system is useful in determining and measuring quantities of bischloromethyl ether, as well as other toxic or non-toxic materials, in extremely minute levels, e.g., in the parts per billion (ppb) level, and especially in a background of parts per million (ppm) level of various other components.

[1]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[2]  Anders Krogh,et al.  A Simple Weight Decay Can Improve Generalization , 1991, NIPS.

[3]  Helge J. Ritter,et al.  Generalization Abilities of Cascade Network Architecture , 1992, NIPS.

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

[5]  John Moody,et al.  Prediction Risk and Architecture Selection for Neural Networks , 1994 .

[6]  Yann LeCun,et al.  Optimal Brain Damage , 1989, NIPS.

[7]  Wray L. Buntine,et al.  Bayesian Back-Propagation , 1991, Complex Syst..

[8]  Anil K. Jain,et al.  Small sample size problems in designing artificial neural networks , 1991 .

[9]  Gregory J. Wolff,et al.  Optimal Brain Surgeon and general network pruning , 1993, IEEE International Conference on Neural Networks.

[10]  Sholom M. Weiss,et al.  Computer Systems That Learn , 1990 .

[11]  Manoel Fernando Tenorio,et al.  Self-organizing network for optimum supervised learning , 1990, IEEE Trans. Neural Networks.

[12]  Joydeep Ghosh,et al.  Evidence combination techniques for robust classification of short-duration oceanic signals , 1992, Defense, Security, and Sensing.

[13]  G. Kane.  Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[14]  John F. Kolen,et al.  Learning in parallel distributed processing networks: Computational complexity and information content , 1991, IEEE Trans. Syst. Man Cybern..

[15]  Terence D. Sanger,et al.  A tree-structured adaptive network for function approximation in high-dimensional spaces , 1991, IEEE Trans. Neural Networks.

[16]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[17]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[18]  E. Littmann.  Generalization Abilities of Cascade Network Architectures , 1992 .

[19]  Sukhan Lee,et al.  A Gaussian potential function network with hierarchically self-organizing learning , 1991, Neural Networks.

[20]  S. Thomas Alexander,et al.  Adaptive Signal Processing , 1986, Texts and Monographs in Computer Science.

[21]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[22]  Vladimir Vapnik,et al.  Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .

[23]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[24]  Andrew R. Barron,et al.  Complexity Regularization with Application to Artificial Neural Networks , 1991 .

[25]  John C. Platt.  A Resource-Allocating Network for Function Interpolation , 1991, Neural Computation.

[26]  Halbert White,et al.  Learning in Artificial Neural Networks: A Statistical Perspective , 1989, Neural Computation.

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

[28]  M. C. Jones,et al.  Spline Smoothing and Nonparametric Regression. , 1989 .

[29]  Russell Reed,et al.  Pruning algorithms-a survey , 1993, IEEE Trans. Neural Networks.

[30]  Vladimir Vapnik,et al.  Principles of Risk Minimization for Learning Theory , 1991, NIPS.

[31]  A. G. Ivakhnenko,et al.  Polynomial Theory of Complex Systems , 1971, IEEE Trans. Syst. Man Cybern..

[32]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[33]  James D. Keeler,et al.  Layered Neural Networks with Gaussian Hidden Units as Universal Approximations , 1990, Neural Computation.

[34]  Joydeep Ghosh,et al.  Approximation of multivariate functions using ridge polynomial networks , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[35]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[36]  Geoffrey E. Hinton,et al.  Learning distributed representations of concepts. , 1989 .

[37]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[38]  G. Lorentz.  Approximation of Functions , 1966 .

[39]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[40]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[41]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[42]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[43]  Yves Chauvin,et al.  A Back-Propagation Algorithm with Optimal Use of Hidden Units , 1988, NIPS.

[44]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[45]  Harry Wechsler,et al.  From Statistics to Neural Networks , 1994, NATO ASI Series.