DESCRIPTION

Package: activelearning
Title: A Collection of Active Learning Methods in R
Version: 0.1.2
Date: 2017-05-21
Author: John A. Ramey
Maintainer: John A. Ramey <johnramey@gmail.com>
Description: 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.
Depends:
    R (>= 3.0.2)
Imports:
    caret (>= 6.0),
    entropy,
    itertools2,
    parallel
License: MIT
Collate:
    'activelearning.r'
    'disagreement.r'
    'helpers.r'
    'oracle.r'
    'query-bagging.r'
    'query-committee.r'
    'query-random.r'
    'uncertainty-sampling.r'
RoxygenNote: 5.0.1