Principal components, minor components, and linear neural networks
暂无分享,去创建一个
[1] E. Oja,et al. Principal component analysis by homogeneous neural networks, Part I : The weighted subspace criterion , 1992 .
[2] Erkki Oja,et al. Principal component analysis by homogeneous neural networks, part II: Analysis and extentions of the learning algorithm , 1992 .
[3] Erkki Oja,et al. Modified Hebbian learning for curve and surface fitting , 1992, Neural Networks.
[4] Kurt Hornik,et al. Convergence analysis of local feature extraction algorithms , 1992, Neural Networks.
[5] Juha Karhunen,et al. Tracking of sinusoidal frequencies by neural network learning algorithms , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.
[6] Didier Le Gall,et al. MPEG: a video compression standard for multimedia applications , 1991, CACM.
[7] Erkki Oja,et al. Neural Nets for Dual Subspace Pattern Recognition Method , 1991, Int. J. Neural Syst..
[8] J. Ariel Sirat,et al. A Fast Neural Algorithm for Principal Component Analysis and Singular Value Decomposition , 1991, Int. J. Neural Syst..
[9] Suzanna Becker,et al. Unsupervised Learning Procedures for Neural Networks , 1991, Int. J. Neural Syst..
[10] G. Golub,et al. Tracking a few extreme singular values and vectors in signal processing , 1990, Proc. IEEE.
[11] Sun-Yuan Kung,et al. A neural network learning algorithm for adaptive principal component extraction (APEX) , 1990, International Conference on Acoustics, Speech, and Signal Processing.
[12] J. Rubner,et al. A Self-Organizing Network for Principal-Component Analysis , 1989 .
[13] Erkki Oja,et al. Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..
[14] Y. Chauvin,et al. Principal component analysis by gradient descent on a constrained linear Hebbian cell , 1989, International 1989 Joint Conference on Neural Networks.
[15] P. Foldiak,et al. Adaptive network for optimal linear feature extraction , 1989, International 1989 Joint Conference on Neural Networks.
[16] Terence D. Sanger,et al. Optimal unsupervised learning in a single-layer linear feedforward neural network , 1989, Neural Networks.
[17] Kurt Hornik,et al. Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.
[18] Ralph Linsker,et al. Self-organization in a perceptual network , 1988, Computer.
[19] E. Oja,et al. Projection filter, Wiener filter, and Karhunen-Loève subspaces in digital image restoration , 1986 .
[20] J. Karhunen. Recursive estimation of eigenvectors of correlation type matrices for signal processing applications , 1985 .
[21] E. Oja,et al. On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix , 1985 .
[22] M. Simaan,et al. IN ThE PRESENCE OF WHITE NOISE , 1985 .
[23] Erkki Oja,et al. Subspace methods of pattern recognition , 1983 .
[24] E. Oja. Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.
[25] Harold J. Kushner,et al. wchastic. approximation methods for constrained and unconstrained systems , 1978 .
[26] J. Pearl,et al. Comparison of the cosine and Fourier transforms of Markov-1 signals , 1976 .
[27] J. Hale,et al. Ordinary Differential Equations , 2019, Fundamentals of Numerical Mathematics for Physicists and Engineers.