Multiple instance boosting with global smoothness regularization
In multiple instance learning, the training set consists of labeled bags that include unlabeled instances, and the target is to predict the labels of unseen bags. A bag is labeled positive only if it contains at least one positive instance, otherwise it is a negative bag. Over the past years, many popular machine learning algorithms have been adapted to tackle the multiple instance learning problems. In this paper, to train a discriminative multiple instance classifier which generalize well, we present a boosting approach with global smoothness regularization, in which the weak learners are either hyper balls with the center at the instance of positive bags or random projection decision stumps. Experimental results show that our proposed algorithm is comparable to the classical Diverse Density algorithm on some multiple instance learning benchmark datasets.