## Prediction risk and architecture selection for neural networks (1994)

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Citations: | 77 - 2 self |

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@INPROCEEDINGS{Moody94predictionrisk,

author = {John Moody},

title = {Prediction risk and architecture selection for neural networks},

booktitle = {},

year = {1994},

pages = {147--165},

publisher = {Springer-Verlag}

}

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### Abstract

Abstract. We describe two important sets of tools for neural network modeling: prediction risk estimation and network architecture selection. Prediction risk is defined as the expected performance of an estimator in predicting new observations. Estimated prediction risk can be used both for estimating the quality of model predictions and for model selection. Prediction risk estimation and model selection are especially important for problems with limited data. Techniques for estimating prediction risk include data resampling algorithms such as nonlinear cross–validation (NCV) and algebraic formulae such as the predicted squared error (PSE) and generalized prediction error (GPE). We show that exhaustive search over the space of network architectures is computationally infeasible even for networks of modest size. This motivates the use of heuristic strategies that dramatically reduce the search complexity. These strategies employ directed search algorithms, such as selecting the number of nodes via sequential network construction (SNC) and pruning inputs and weights via sensitivity based pruning (SBP) and optimal brain damage (OBD) respectively.

### Citations

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Citation Context ..., for example generalized cross--validation (GCV) (Craven and Wahba, 1979; Golub, Heath and Wahba, 1979), Akaike's final prediction error (FPE) (Akaike, 1970), Akaike's information criterion A (AIC) (=-=Akaike, 1973-=-), and predicted squared error (PSE) (see discussion in Barron (1984)), and the recently proposed generalized prediction error (GPE) for nonlinear models (Moody (1991; 1992; 1995)). 2 Prediction Risk ... |

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Citation Context ...quared error loss function, a number of useful algebraic estimates for the prediction risk have been derived. These include the well known generalized cross--validation (GCV) (Craven and Wahba, 1979; =-=Golub et al., 1979-=-) and Akaike's final prediction error (FPE) (Akaike, 1970) formulas: GCV () = ASE() 1 i 1 \Gamma Q() N j 2 FPE() = ASE() / 1 + Q() N 1 \Gamma Q() N ! : (8) Q() denotes the number of weights of model .... |

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Citation Context ...e and should be considered when constructing neural network models. These include the irrelevant hidden unit and irrelevant input hypothesis tests (White, 1989), pruning of units via skeletonization (=-=Mozer and Smolensky, 1990-=-), optimal brain surgeon OBS (Hassibi and Stork, 1993), and principal components pruning PCP (Levin et al., 1994). It is important to note that all these methods, along with OBD and our method of inpu... |

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Citation Context ...hich "best fits" the data. The notion of "best fits" can be precisely defined via an objective criterion, such as maximum a posteriori probability (MAP), minimum Bayesian informati=-=on criterion (BIC) (Akaike, 1977-=-; Schwartz, 1978), minimum description length (MDL) (Rissanen, 1978), or minimum prediction risk (P). In this paper, we focus on the prediction risk as our selection criterion for two reasons. First, ... |

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Citation Context ...vant input hypothesis tests (White, 1989), pruning of units via skeletonization (Mozer and Smolensky, 1990), optimal brain surgeon OBS (Hassibi and Stork, 1993), and principal components pruning PCP (=-=Levin et al., 1994-=-). It is important to note that all these methods, along with OBD and our method of input pruning via SBP, are closely related to the Wald hypothesis testing procedure (see for example Buse (1982)). I... |

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