Statistical Aspects of Generalization in Neural Networks
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[1] Ming Li,et al. An Introduction to Kolmogorov Complexity and Its Applications , 2019, Texts in Computer Science.
[2] H. Akaike. Fitting autoregressive models for prediction , 1969 .
[3] Yaser S. Abu-Mostafa,et al. The Vapnik-Chervonenkis Dimension: Information versus Complexity in Learning , 1989, Neural Computation.
[4] Yann LeCun,et al. Optimal Brain Damage , 1989, NIPS.
[5] Leslie G. Valiant,et al. A theory of the learnable , 1984, STOC '84.
[6] Vladimir Vapnik,et al. Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .
[7] Lars Kai Hansen,et al. On design and evaluation of tapped-delay neural network architectures , 1993, IEEE International Conference on Neural Networks.
[8] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[9] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[10] James A. Anderson,et al. Neurocomputing: Foundations of Research , 1988 .
[11] Gregory J. Wolff,et al. Optimal Brain Surgeon and general network pruning , 1993, IEEE International Conference on Neural Networks.
[12] W S McCulloch,et al. A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.
[13] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..
[14] Anders Krogh,et al. Introduction to the theory of neural computation , 1994, The advanced book program.
[15] Jukka Saarinen,et al. Chaotic time series modeling with optimum neural network architecture , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).
[16] Jukka Saarinen,et al. Neural Network Modeling and Prediction of Multivariate Time Series Using Predictive MDL Principle , 1993 .
[17] David Haussler,et al. Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.