Optimal Stopping and Effective Machine Complexity in Learning

By Changfeng Wang, Santosh S. Venkatesh, J. S. Judd. 1993

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Deep learning in neural networks: An overview (2014, background): “, 1979), final prediction error (Akaike, 1970), generalized prediction error (Moody and Utans, 1994; Moody, 1992); see also (Holden, 1994; Wang et al., 1994; Amari and Murata, 1993; Wang et al., 1994; Guyon et al., 1992; Vapnik, 1992; Wolpert, 1994).”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, background): “2 M ’s Training To decrease bitsM + bitsH , we add a regularizing term to E, to punish excessive complexity [4, 5, 91, 294, 155, 135, 97, 170, 169, 104, 290, 7, 290, 87, 286, 319, 100, 102].”
Early Stopping-But When? (1996, methodology): “[24] or the alternative view induced by the bias/variance decomposition of the error as described by Geman et al.”
Asymptotic statistical theory of overtraining and cross-validation (1997, methodology): “This training procedure is usually carried out by a gradient descent method minimizing a target function ([1], [27], and many others).”