Multiple-instance learning

"Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled negative only if all the instances in it are negative."

"The idea for multiple instance learning was originally proposed 1990 for handwritten digit recognition by Keeler, et.al. Keeler's approach was called Integrated Segmentation and Recognition (ISR)."

"Another relevant example of MIL is the Diverse Density approach of Maron. Diverse Density uses the Noisy OR generative model to explain the bag labels. A gradient-descent algorithm is used to find the best point in input space that explains the positive bags."

A lot of researches are conduct on adapt classical classifiers like SVM, Boost and so on with MIL.