Neural networks: a pattern recognition perspective
暂无分享,去创建一个
[1] E. M. Wright,et al. Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.
[2] E. Polak,et al. Computational methods in optimization : a unified approach , 1972 .
[3] Keinosuke Fukunaga,et al. Introduction to Statistical Pattern Recognition , 1972 .
[4] Y. Chien,et al. Pattern classification and scene analysis , 1974 .
[5] M. Stone. Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .
[6] G. Wahba,et al. A completely automatic french curve: fitting spline functions by cross validation , 1975 .
[7] M. Stone. Cross-validation:a review 2 , 1978 .
[8] David J. Hand,et al. Discrimination and Classification , 1982 .
[9] J. Friedman,et al. Projection Pursuit Regression , 1981 .
[10] Philip E. Gill,et al. Practical optimization , 1981 .
[11] Josef Kittler,et al. Pattern recognition : a statistical approach , 1982 .
[12] John E. Dennis,et al. Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.
[13] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[14] E. T. Jaynes,et al. BAYESIAN METHODS: GENERAL BACKGROUND ? An Introductory Tutorial , 1986 .
[15] Robin Sibson,et al. What is projection pursuit , 1987 .
[16] J J Hopfield,et al. Learning algorithms and probability distributions in feed-forward and feed-back networks. , 1987, Proceedings of the National Academy of Sciences of the United States of America.
[17] Eric B. Baum,et al. Supervised Learning of Probability Distributions by Neural Networks , 1987, NIPS.
[18] R. Fletcher. Practical Methods of Optimization , 1988 .
[19] James A. Anderson,et al. Neurocomputing: Foundations of Research , 1988 .
[20] Esther Levin,et al. Accelerated Learning in Layered Neural Networks , 1988, Complex Syst..
[21] David S. Broomhead,et al. Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..
[22] John Moody,et al. Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.
[23] Ken-ichi Funahashi,et al. On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.
[24] Hecht-Nielsen. Theory of the backpropagation neural network , 1989 .
[25] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..
[26] Geoffrey E. Hinton. Connectionist Learning Procedures , 1989, Artif. Intell..
[27] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[28] R. Hecht-Nielsen,et al. Theory of the Back Propagation Neural Network , 1989 .
[29] H. White,et al. Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions , 1989, International 1989 Joint Conference on Neural Networks.
[30] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[31] Kurt Hornik,et al. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks , 1990, Neural Networks.
[32] Neil E. Cotter,et al. The Stone-Weierstrass theorem and its application to neural networks , 1990, IEEE Trans. Neural Networks.
[33] Vladik Kreinovich,et al. Arbitrary nonlinearity is sufficient to represent all functions by neural networks: A theorem , 1991, Neural Networks.
[34] Yoshifusa Ito,et al. Representation of functions by superpositions of a step or sigmoid function and their applications to neural network theory , 1991, Neural Networks.
[35] Barak A. Pearlmutter,et al. Equivalence Proofs for Multi-Layer Perceptron Classifiers and the Bayesian Discriminant Function , 1991 .
[36] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[37] Chris Bishop,et al. Current address: Microsoft Research, , 2022 .
[38] David J. C. MacKay,et al. The Evidence Framework Applied to Classification Networks , 1992, Neural Computation.
[39] David J. C. MacKay,et al. Bayesian Interpolation , 1992, Neural Computation.
[40] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.
[41] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[42] C. Bishop. Mixture density networks , 1994 .
[43] Yves Chauvin,et al. Backpropagation: the basic theory , 1995 .
[44] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .