The bootstrap is a powerful tool for inference in statistical models. This note describes briefly what the bootstrap is and how to use it. Suppose X1, . . . , Xn is an i.i.d. sample drawn from an unknown distribution P . Consider testing the null hypothesis H0 : θ = θ0 versus the alternative H1 : θ 6= θ0 where θ is a parameter related to the distribution P . The first step is to construct a tes...

We present a novel approach for density estimation using Bayesian networks when faced with scarce and partially observed data. Our approach relies on Efron’s bootstrap framework, and replaces the standard model selection score by a bootstrap aggregation objective aimed at sifting out bad decisions during the learning procedure. Unlike previous bootstrap or MCMC based approaches that are only ai...

We present a novel approach for density estimation using Bayesian networks when faced with scarce and partially observed data. Our approach relies on Efron’s bootstrap framework, and replaces the standard model selection score by a bootstrap aggregation objective aimed at sifting out bad decisions during the learning procedure. Unlike previous bootstrap or MCMC based approaches that are only ai...

Bagging and boosting, two effective machine learning techniques, are applied to natural language parsing. Experiments using these techniques with a trainable statistical parser are described. The best resulting system provides roughly as large of a gain in F-measure as doubling the corpus size. Error analysis of the result of the boosting technique reveals some inconsistent annotations in the P...

In classification problems, there are several attempts to create rules which assign future observations to certain classes. Common methods are for example linear discriminant analysis or classification trees. Recent developments lead to substantial reduction of misclassification error in many applications. Bootstrap aggregation (“bagging”, Breiman, 1996a) combines classifiers trained on bootstr...

NETWORKS dentifying customers who are likely to respond to a product offering is an important issue in direct marketing.Response models are typically built from historical purchase data. A popular method of choice, logistic regression, is easy to understand and build, but limited in that the model is linear in parameters. Neural networks are nonlinear and have been found to improve predictive a...