Architecture Selection Strategies for Neural Networks: Application to Corporate Bond Rating Predicti

By John E. Moody, Joachim Utans. 1995

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Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “…sets (Craven & Wahba, 1979; Eubank, 1988; Golub, Heath, & Wahba, 1979; Hastie & Tibshirani, 1990; Mosteller & Tukey, 1968; Stone, 1974), Akaike’s information criterion and final prediction error (Akaike, 1970, 1973, 1974), or generalized prediction error (Moody, 1992; Moody & Utans, 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].”
NARX-Based Nonlinear System Identification Using Orthogonal Least Squares Basis Hunting (2008, unknown): “The termination of the OLS-BH process can also be decided using cross validation [29]–[31].”
Neural networks for classification: a survey (2000, background): “…research topics related to neural classification include network training [12], [15], [62], [124], [142], model design and selection [50], [72], [117], [121], [122], [180], [194], sample size issues [51], [135], [136], Bayesian analysis [102], [109], [110], [120], and wavelet networks [165],…”
Flat Minima (1997, unknown): “…based on validation sets (Mosteller & Tukey, 1968; Stone, 1974; Eubank, 1988; Hastie & Tibshirani, 1993), e.g., \generalized cross validation" (Craven & Wahba, 1979; Golub et al., 1979), \\nal prediction error" (Akaike, 1970), \generalized prediction error" (Moody & Utans, 1994; Moody, 1992 ).”
Modified support vector machines in financial time series forecasting (2002, background): “Over the past few years, neural networks have been successfully used for modeling nancial time series ranging from options price [5], corporate bond rating [9], stock index trading [8] to currency exchange [24].”
Credit rating analysis with support vector machines and neural networks: a market comparative study (2004, methodology): “Moody and Utans [30] used neural networks to predict 16 categories of S and P rating ranging from ''B-'' and below (3) to ''AAA'' (18).”