The multiple-instance learning model is becoming increasingly important in machine learning. Unlike standard supervised learning in which each instance is labeled in the training data, here each example is a set (or bag) of instances which receives a single label equal to the maximum label among the instances in the bag. The individual instances are not given a label. The goal of the learner is to