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AdaBoost also increases the margin of the training data, reducing generalization error even after training error is zero Self-training and
AdaBoost also increases the margin of the training data, reducing generalization error even after training error is zero Self-training and co-training use ensembles of learners to take advantage of under-annotated training data Committee machines generally outperform all but the best single learners, but by any measure are more complex So what about Occams Razor? What happened to the curse of dimensionality? Dimensionality (as number of features) has no clear interpretation for complex modeling procedures And what about simplicity? Didnt we say simpler = lower variance? How is an ensemble of trees simpler than a single tree? How is MaxEnt simpler than Naive Bayes? How is MaxEnt+prior simpler than MaxEnt?
