Multiple Instance Learning: Algorithms and Applications

Traditional supervised learning requires a training data set that consists of inputs and corresponding labels. In many applications, however, it is difficult or even impossible to accurately and consistently assign labels to inputs. A relatively new learning paradigm called Multiple Instance Learning allows the training of a classifier from ambiguously labeled data. This paradigm has been receiving much attention in the last several years, and has many useful applications in a number of domains (e.g. computer vision, computer audition, bioinformatics, text processing). In this report we review several representative algorithms that have been proposed to solve this problem. Furthermore, we discuss a number of existing and potential applications, and how well the currently available algorithms address the problems presented by these applications.