An Active Learning Formulation for Instance Selection with Applications to Object Detection

Abstract

In certain real-world learning scenarios where there are enormous amounts of training data, the training process can become computationally intractable. Researchers have attempted to address this problem by performing instance selection, which is to automatically identify and preserve a sufficiently small but highly informative data sample for training. In most classical formulations of example-based learning, the learner passively receives randomly drawn training examples from which it recovers the unknown target function. Active Learning describes a different example-based learning paradigm where the learner explicitly seeks for new training examples of high utility, and can thus be viewed as a form of instance selection. This chapter presents a Bayesian formulation for active learning within a classical function approximation learning framework, and shows how one can derive precise example selection algorithms for learning some simple target function classes more accurately with less training data. We then present a real-world learning scenario on object (face) detection, and show how our active learning formulation leads to a useful instance selection heuristic for identifying and retaining high utility training data.