There are two common intuitions about how this learning process should be organized: (i) by choosing query points that shrink the space of candidate classifiers as rapidly as possible; and (ii) by exploiting natural clusters in the (unlabeled) data set.
Abstract. An active learner has a collection of data points, each with a label that is initially hidden but can be obtained at some cost.
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An active learner has a collection of data points, each with a label that is initially hidden but can be obtained at some cost. Without spending too much, ...
The active learning model is motivated by scenarios in which it is easy to amass vast quantities of unlabeled data (images and videos off the web, ...
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The active learning model is motivated by scenarios in which it is easy to amass vast quantities of unlabeled data (images and videos off the web, ...
This work proposes a novel nonparametric algorithm, ANDA, that combines an active nearest neighbor querying strategy with nearest neighbor prediction and ...
... The most widely used approaches to acquiring data for AL are based on uncertainty and diversity, often described as the "two faces of AL" (Dasgupta, 2011) .
The two faces of active learning. Author: Sanjoy Dasgupta. Sanjoy ... Index Terms. The two faces of active learning. Computing methodologies · Machine learning.
TL;DR: The active learning model is motivated by scenarios in which it is easy to amass vast quantities of unlabeled data but costly to obtain their labels, ...
In the Active Learning phase, algorithm starts to sample points closer to the boundary, getting further from the real distribution P.