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Last update: T_KSI (07.05.2002)
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Last update: T_KSI (05.05.2004)
M. Anthony, P.L. Bartlett: Neural Network Learning: Theoretical Foundations. Cambridge, UK: Cambridge University Press, 1999.
V.P. Roychowdhury, K.-Y. Siu, A. Orlitsky (eds.): Theoretical Advances in Neural Computation and Learning. Boston: Kluwer Academic Publishers, 1994
K.-Y. Siu, V.P. Roychowdhury, T. Kailath: Discrete Neural Computation: A Theoretical Foundation. Englewood Cliffs. NJ: Prentice Hall, 1995.
J. Sima, R. Neruda: Teoreticke otazky neuronovych siti. Praha: MATFYZPRESS, 1996. |
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Last update: T_KSI (07.05.2002)
1. Perceptron: integer representation, weight size, linear separability problem.
2. Feedforward networks: implementation of arithmetic and logical functions, universal threshold circuit, $TC^0$-hierarchy and its separation for small depths, total wire length, analog and probabilistic circuits.
3. Recurrent networks: neural language acceptors and Kolmogorov complexity of weights, infinite families of networks, probabilistic models.
4. Hopfield networks: convergence time, stable states, energy minimization, computational power, continuous time.
5. Alternative models: RBF networks, Kohonen networks, spiking neurons.
6. Learning complexity: loading problem, sample complexity and VC-dimension, PAC model.
LITERATURA:
M. Anthony, P.L. Bartlett: Neural Network Learning: Theoretical Foundations. Cambridge, UK: Cambridge University Press, 1999.
V.P. Roychowdhury, K.-Y. Siu, A. Orlitsky (eds.): Theoretical Advances in Neural Computation and Learning. Boston: Kluwer Academic Publishers, 1994.
K.-Y. Siu, V.P. Roychowdhury, T. Kailath: Discrete Neural Computation: A Theoretical Foundation. Englewood Cliffs, NJ: Prentice Hall, 1995.
J. Sima, R. Neruda: Teoreticke otazky neuronovych siti. Praha: MATFYZPRESS, 1996. |