Neural Networks for Pattern Recognition
暂无分享,去创建一个
[1] Heekuck Oh,et al. A pseudo-relaxation learning algorithm for bidirectional associative memory , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.
[2] Heekuck Oh,et al. A new learning approach to enhance the storage capacity of the Hopfield model , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.
[3] Jose B. Cruz,et al. Guaranteed recall of all training pairs for bidirectional associative memory , 1991, IEEE Trans. Neural Networks.
[4] S. Sitharama Iyengar,et al. Neurocomputing Formalisms for Computational Learning and Machine Intelligence , 1991, Adv. Comput..
[5] K Fukushima,et al. Handwritten alphanumeric character recognition by the neocognitron , 1991, IEEE Trans. Neural Networks.
[6] Jehoshua Bruck. On the convergence properties of the Hopfield model , 1990, Proc. IEEE.
[7] Jose B. Cruz,et al. Two coding strategies for bidirectional associative memory , 1990, IEEE Trans. Neural Networks.
[8] Mohamad T. Musavi,et al. A neural network approach to character recognition , 1989, Neural Networks.
[9] Bruno Cernuschi-Frías,et al. Partial simultaneous updating in Hopfield memories , 1989, IEEE Trans. Syst. Man Cybern..
[10] Mohamad H. Hassoun. Dynamic heteroassociative neural memories , 1989, Neural Networks.
[11] Gail A. Carpenter,et al. Neural network models for pattern recognition and associative memory , 1989, Neural Networks.
[12] Alex Waibel,et al. Consonant recognition by modular construction of large phonemic time-delay neural networks , 1989, International Conference on Acoustics, Speech, and Signal Processing,.
[13] J. Slawny,et al. Back propagation fails to separate where perceptrons succeed , 1989 .
[14] Geoffrey E. Hinton,et al. Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..
[15] Robert Hecht-Nielsen,et al. Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.
[16] Eduardo D. Sontag,et al. Backpropagation Can Give Rise to Spurious Local Minima Even for Networks without Hidden Layers , 1989, Complex Syst..
[17] Christian Lebiere,et al. The Cascade-Correlation Learning Architecture , 1989, NIPS.
[18] John J. Hopfield. Collective computation, content-addressable memory, and optimization problems , 1988 .
[19] Sweet. Determination of parameters in a Hopfield/Tank computational network , 1988 .
[20] B. Kosko,et al. Feedback stability and unsupervised learning , 1988, IEEE 1988 International Conference on Neural Networks.
[21] Robert Hecht-Nielsen,et al. A BAM with increased information storage capacity , 1988, IEEE 1988 International Conference on Neural Networks.
[22] Alex Waibel,et al. Phoneme recognition: neural networks vs. hidden Markov models vs. hidden Markov models , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.
[23] B. Forrest. Content-addressability and learning in neural networks , 1988 .
[24] E. Gardner,et al. Optimal storage properties of neural network models , 1988 .
[25] BART KOSKO,et al. Bidirectional associative memories , 1988, IEEE Trans. Syst. Man Cybern..
[26] Bernard Widrow,et al. Adaptive switching circuits , 1988 .
[27] Kunihiko Fukushima,et al. Neocognitron: A hierarchical neural network capable of visual pattern recognition , 1988, Neural Networks.
[28] D. J. Wallace,et al. Implementing Neural Network Models on Parallel Computers , 1987, Comput. J..
[29] Santosh S. Venkatesh,et al. The capacity of the Hopfield associative memory , 1987, IEEE Trans. Inf. Theory.
[30] Bart Kosko,et al. Optical Bidirectional Associative Memories , 1987, Photonics West - Lasers and Applications in Science and Engineering.
[31] Richard Durbin,et al. An analogue approach to the travelling salesman problem using an elastic net method , 1987, Nature.
[32] R. Lippmann,et al. An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.
[33] B. Kosco. Differential Hebbian learning , 1987 .
[34] Wr Jeffrey,et al. Neural network processing as a tool for function optimization , 1987 .
[35] D. Kleinfeld,et al. "Unlearning" increases the storage capacity of content addressable memories. , 1987, Biophysical journal.
[36] Kanter,et al. Associative recall of memory without errors. , 1987, Physical review. A, General physics.
[37] Gene A. Tagliarini,et al. A Neural-Network Solution to the Concentrator Assignment Problem , 1987, NIPS.
[38] A. Harry Klopf,et al. A drive-reinforcement model of single neuron function , 1987 .
[39] Geoffrey E. Hinton,et al. Learning symmetry groups with hidden units: beyond the perceptron , 1986 .
[40] John J. Hopfield,et al. Simple 'neural' optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit , 1986 .
[41] R. Golden. The :20Brain-state-in-a-box Neural model is a gradient descent algorithm , 1986 .
[42] Geoffrey E. Hinton,et al. Learning and relearning in Boltzmann machines , 1986 .
[43] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[44] L. Rabiner,et al. An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.
[45] Eric Goles Ch.,et al. Dynamics of Positive Automata Networks , 1985, Theor. Comput. Sci..
[46] Eric Goles Ch.,et al. Decreasing energy functions as a tool for studying threshold networks , 1985, Discret. Appl. Math..
[47] Gérard Weisbuch,et al. Scaling laws for the attractors of Hopfield networks , 1985 .
[48] Yaser S. Abu-Mostafa,et al. Information capacity of the Hopfield model , 1985, IEEE Trans. Inf. Theory.
[49] I. Guyon,et al. Information storage and retrieval in spin-glass like neural networks , 1985 .
[50] Geoffrey E. Hinton,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..
[51] Leslie G. Valiant,et al. A theory of the learnable , 1984, STOC '84.
[52] 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.
[53] Francis Crick,et al. The function of dream sleep , 1983, Nature.
[54] J. J. Hopfield,et al. ‘Unlearning’ has a stabilizing effect in collective memories , 1983, Nature.
[55] 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.
[56] A G Barto,et al. Toward a modern theory of adaptive networks: expectation and prediction. , 1981, Psychological review.
[57] T. Sejnowski,et al. Storing covariance with nonlinearly interacting neurons , 1977, Journal of mathematical biology.
[58] S. Amari,et al. A Mathematical Foundation for Statistical Neurodynamics , 1977 .
[59] Shun-ichi Amari,et al. Learning Patterns and Pattern Sequences by Self-Organizing Nets of Threshold Elements , 1972, IEEE Transactions on Computers.
[60] S. Grossberg. On learning and energy-entropy dependence in recurrent and nonrecurrent signed networks , 1969 .
[61] S Grossberg,et al. Some nonlinear networks capable of learning a spatial pattern of arbitrary complexity. , 1968, Proceedings of the National Academy of Sciences of the United States of America.
[62] Thomas M. Cover,et al. Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition , 1965, IEEE Trans. Electron. Comput..
[63] S. Agmon. The Relaxation Method for Linear Inequalities , 1954, Canadian Journal of Mathematics.
[64] I. J. Schoenberg,et al. The Relaxation Method for Linear Inequalities , 1954, Canadian Journal of Mathematics.