Multiple instance learning using simple classifiers
Adam Cannon, Donald R. Hush · 2005
In this paper we study Multiple Instance Learning, a variant of the standard classification problem. We demonstrate the utility of an empirical risk minimization approach allowing for a straightforward classification treatment of the problem. In addition we consider simple data dependent hypothesis classes that allow efficient minimization of the empirical loss function and the development of bounds on the estimation error. Our empirical results are competitive with those of the most successful previously published methods.