Active learning is a machine learning paradigm for optimally choosing unlabeled observations in a training data set to query for their true labels. The framework is particularly useful when there are very few labeled observations relative to a large number of unlabeled observations, and the user seeks to determine as few true labels as possible to achieve highly accurate classifiers. This package is a collection of various active learning methods from the literature to optimally query observations with respect to a variety of objective functions. Some active learning methods require posterior probability estimates of the unlabeled observations from a single classifier or a committee of classifiers; this package allows the user to specify custom classifiers. An excellent literature survey has been provided by Dr. Burr Settles.
Package details |
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Author | John A. Ramey |
Maintainer | John A. Ramey <johnramey@gmail.com> |
License | MIT |
Version | 0.1.2 |
Package repository | View on GitHub |
Installation |
Install the latest version of this package by entering the following in R:
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