TCS: a shell for content-based text categorization
Proceedings of the sixth conference on Artificial intelligence applications
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
A statistical perspective on data mining
Future Generation Computer Systems - Special double issue on data mining
Predictive data mining: a practical guide
Predictive data mining: a practical guide
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
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Predictive models have been widely used long before the development of the new field that we call data mining. Expanding application demand for data mining of ever increasing data warehouses, and the need for understandability of predictive models with increased accuracy of prediction, all have fueled recent advances in automated predictive methods. We first examine a few successful application areas and technical challenges they present. We discuss some theoretical developments in PAC learning and statistical learning theory leading to the emergence of support vector machines. We then examine some technical advances made in enhancing the performance of the models both in accuracy (boosting, bagging, stacking) and scalability of modeling through distributed model generation.