Cross-validation, bootstrap, and support vector machines
Advances in Artificial Neural Systems
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In this article logistic regression model is highlighted using a feedforward neural network model. Suggested here is a simple more precise prognostic technique by incorporating follow-up data in the development of the model when the data consist of grouped binary response and a set of predictor variables, which is closely related to classical logistic regression model. Statistical techniques are formulated in terms of the principle of the likelihood when a resampling process such as bootstrapping and cross-validation is applied. We then attempt to determine the number of units in the hidden layer, to verify the asymptotic χ2 behavior of the deviance for the goodness-of-fit test of the model, to detect inappropriate influential observations and outliers, and to improve the goodness-of-fit test of the neural network model. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(6): 13–20, 2006; Published online in Wiley InterScience (). DOI 10.1002/scj.20497