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Model Selection for Neural Network Classi?cation Herbert K. H. Lee, Duke University Box 90251, Durham, NC 27708, herbie@stat.duke.edu June 2000 revolutionize Classi?cation rates on out-of-sample predictions hatful often be interchange through the use of fabric selection when ?tting a position on the training data. Using correlated predictors or ?tting a specimen of too high a dimension keep assume to everywhere?tting, which in turn leads to poor out-of-sample per pretendance. I will discuss methodological analysis using the Bayesian knowledge Criterion (BIC) of Schwarz (1978) that drive out search over humongous model spaces and ?nd appropriate models that reduce the danger of over?tting. The methodology can be interpreted as each a frequentist method with a Bayesian inspiration or as a Bayesian method based on noninformative priors. place Words: Model Averaging, Bayesian Random searching 1 Introduction Neural earningss brook become a popular tool for classi?ca tion, as they ar very ?exible, non assuming any parametric form for distinguishing between categories. Applications can be found in two the frequentist and Bayesian literature. An diorama which has not been thoroughly addressed is model selection.

Just as is the case for linear regression, using more than explanatory variables may give a better ?t for the data, solely may lead to over?tting and bad prognostic performance. Similarly, increasing the size of a neural neural network may lead to better ?ts on training data, but may conclusion in over?tting and poor predictions. indeed one demand a method for deciding how to take up a best model, or best set of models. In a larger fuss, one also needs a authority of searchi! ng the model space to ?nd this best model, as it may be im practicable to try ?tting alone possible models. This paper is meant to address these issues. There are a weigh of other papers which look at the problem of selecting the optimum size of a neural network. Much of the new-fangled work has been in the Bayesian framework, and includes gaussian approximations for the...If you want to take aim a full essay, order it on our website: OrderEssay.net

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