An introduction to computational learning theory
An introduction to computational learning theory
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Statistical Themes and Lessons for Data Mining
Data Mining and Knowledge Discovery
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This paper describes a flexible model for predictive data mining, EGB2, which optimizes over a parameter space to fit data to a family of models based on maximum-likelihood criteria. It is also shown how EGB2 can integrate asymmetric costs of Type I and Type II errors, thereby minimizing expected misclassification costs.Importantly, it has been shown that standard methods of computing maximum-likelihood estimators are generally inconsistent when applied to sample data having different proportions of labels than are found in the universe from which the sample is drawn. We show how a choice estimator based on weighting each observation's contribution to the log-likelihood function, can contribute to estimator consistency and how this feature can be implemented in EGB2.