  How can we find a general way to choose the most suitable samples for
training a classifier? Even with very limited prior information? Active
learning, which can be regarded as an iterative optimization procedure, plays a
key role to construct a refined training set to improve the classification
performance in a variety of applications, such as text analysis, image
recognition, social network modeling, etc.
  Although combining representativeness and informativeness of samples has been
proven promising for active sampling, state-of-the-art methods perform well
under certain data structures. Then can we find a way to fuse the two active
sampling criteria without any assumption on data? This paper proposes a general
active learning framework that effectively fuses the two criteria. Inspired by
a two-sample discrepancy problem, triple measures are elaborately designed to
guarantee that the query samples not only possess the representativeness of the
unlabeled data but also reveal the diversity of the labeled data. Any
appropriate similarity measure can be employed to construct the triple
measures. Meanwhile, an uncertain measure is leveraged to generate the
informativeness criterion, which can be carried out in different ways.
  Rooted in this framework, a practical active learning algorithm is proposed,
which exploits a radial basis function together with the estimated
probabilities to construct the triple measures and a modified
Best-versus-Second-Best strategy to construct the uncertain measure,
respectively. Experimental results on benchmark datasets demonstrate that our
algorithm consistently achieves superior performance over the state-of-the-art
active learning algorithms.
