Government Watch: Bagging plastic

Bagging Predictors Bagging Predictors

Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and ...

Watch What I Watch

This paper presents a high-level overview of Yahoo Research Berkeley’s approach to multimedia research and the ideas motivating it. This approach is characterized primarily by a shift away from building subsystems that attempt to discover or understand the “meaning” of media content toward systems and algorithms that can usefully utilize information about how media content is being used in spec...

Bagging Regularizes

Intuitively, we expect that averaging — or bagging — different regressors with low correlation should smooth their behavior and be somewhat similar to regularization. In this note we make this intuition precise. Using an almost classical definition of stability, we prove that a certain form of averaging provides generalization bounds with a rate of convergence of the same order as Tikhonov regu...

Analyzing Bagging

Bagging is one of the most effective computationally intensive procedures to improve on unstable estimators or classifiers, useful especially for high dimensional data set problems. Here we formalize the notion of instability and derive theoretical results to analyze the variance reduction effect of bagging (or variants thereof) in mainly hard decision problems, which include estimation after t...

Explaining Bagging