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Traditional sampling and statistical learning theories deal with data collection processes that are completely independent of the target function to be estimated, aside from possible a priori specifications reflective of assumed properties of the target. We refer to such processes as passive learnin ...

Tree models are widely used for regression and classification problems, with interpretability and ease of implementation being among their chief attributes. Despite the widespread use tree models, a comprehensive theoretical analysis of their performance has only begun to emerge in recent years. ...

Traditional approaches to machine learning and statistical inference are passive, in the sense that all data are collected prior to analysis in a non-adaptive fashion. One can envision, however more active strategies in which information gleaned from previously collected data is used to guide the se ...

The aim of active learning is to reduce the number of labeled examples needed to learn a good prediction rule by sequentially and adaptively selecting examples for labeling. The basic idea is to use knowledge gained from previously labeled examples to automatically select the most informative examp ...