Least mean square error reconstruction principle for self-organizing neural-nets

[1]  Kurt Hornik,et al.  Backpropagation and unsupervised learning in linear networks , 1995 .

[2]  Erkki Oja,et al.  Rival penalized competitive learning for clustering analysis, RBF net, and curve detection , 1993, IEEE Trans. Neural Networks.

[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]  Geoffrey E. Hinton,et al.  Self-organizing neural network that discovers surfaces in random-dot stereograms , 1992, Nature.

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

[7]  Lei Xu.  Least MSE reconstruction for self-organization. II. Further theoretical and experimental studies on one-layer nets , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

[8]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[9]  R. Brockett.  Dynamical systems that sort lists, diagonalize matrices, and solve linear programming problems , 1991 .

[10]  Geoffrey E. Hinton,et al.  Evaluation of Adaptive Mixtures of Competing Experts , 1990, NIPS.

[11]  Tomas Hrycej,et al.  Self-organization by delta rule , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[12]  S. Y. Kung,et al.  Constrained principal component analysis via an orthogonal learning network , 1990, IEEE International Symposium on Circuits and Systems.

[13]  Stanley C. Ahalt,et al.  Competitive learning algorithms for vector quantization , 1990, Neural Networks.

[14]  T Poggio,et al.  Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks , 1990, Science.

[15]  Stephen Grossberg,et al.  ART 3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures , 1990, Neural Networks.

[16]  J. Rubner,et al.  A Self-Organizing Network for Principal-Component Analysis , 1989 .

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

[18]  Martin Casdagli,et al.  Nonlinear prediction of chaotic time series , 1989 .

[19]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[20]  Erkki Oja,et al.  Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..

[21]  Y. Chauvin,et al.  Principal component analysis by gradient descent on a constrained linear Hebbian cell , 1989, International 1989 Joint Conference on Neural Networks.

[22]  P. Foldiak,et al.  Adaptive network for optimal linear feature extraction , 1989, International 1989 Joint Conference on Neural Networks.

[23]  Terence D. Sanger,et al.  Optimal unsupervised learning in a single-layer linear feedforward neural network , 1989, Neural Networks.

[24]  Kurt Hornik,et al.  Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.

[25]  Duane DeSieno,et al.  Adding a conscience to competitive learning , 1988, IEEE 1988 International Conference on Neural Networks.

[26]  Ralph Linsker,et al.  Self-organization in a perceptual network , 1988, Computer.

[27]  Stephen Grossberg,et al.  Art 2: Self-Organization Of Stable Category Recognition Codes For Analog Input Patterns , 1988, Other Conferences.

[28]  Stephen Grossberg,et al.  The ART of adaptive pattern recognition by a self-organizing neural network , 1988, Computer.

[29]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[30]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[31]  Stephen Grossberg,et al.  Competitive Learning: From Interactive Activation to Adaptive Resonance , 1987, Cogn. Sci..

[32]  R. Hecht-Nielsen.  Counterpropagation networks. , 1987, Applied optics.

[33]  R Linsker,et al.  From basic network principles to neural architecture: emergence of orientation columns. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[34]  R Linsker,et al.  From basic network principles to neural architecture: emergence of orientation-selective cells. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[35]  R Linsker,et al.  From basic network principles to neural architecture: emergence of spatial-opponent cells. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[36]  R. Linsker,et al.  From basic network principles to neural architecture , 1986 .

[37]  David Zipser,et al.  Feature Discovery by Competive Learning , 1986, Cogn. Sci..

[38]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[39]  Stephen Grossberg,et al.  Absolute stability of global pattern formation and parallel memory storage by competitive neural networks , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[40]  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.

[41]  E. Bienenstock,et al.  Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[42]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[43]  E. Oja.  Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.

[44]  Roman Bek,et al.  Discourse on one way in which a quantum-mechanics language on the classical logical base can be built up , 1978, Kybernetika.

[45]  S. Grossberg.  Contour Enhancement , Short Term Memory , and Constancies in Reverberating Neural Networks , 1973 .

[46]  S. Grossberg.  On learning and energy-entropy dependence in recurrent and nonrecurrent signed networks , 1969 .

[47]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.