Budgeted Learning
In some machine learning applications,
the learning algorithms have access to the labels of the
training data for free but have to pay to "see" the
attribute values of those data. This problem is, in a
sense, a dual to active learning. Budgeted learning
algorithms attempt to intelligently choose the attributes
to purchase in order to learn well with as few purchased
attributes as possible.
Active
Learning
In some machine learning applications, the learning
algorithms are given abundant amounts of unlabeled data
with an oracle that is capable of labeling a relatively
small number of these examples to be used in supervised
training. Active learning algorithms attempt to
intelligently choose the examples to label in order to
learn well with as few labeled examples as possible.
Receiver Operating
Characteristic (ROC) Analysis
Receiver Operating Characteristic (ROC) analysis is an
alternative means of measuring classifier peroformance. We
study mechanisms of adjusting multi-class classifiers to
improve performance (with respect to its ROC hypersurface)
when faced with nonuniform misclassification costs.