A Review of Multi-Instance Learning Assumptions <s> MI Learning in Other Supervised Settings <s> We present a Multiple-Instance Learning (MIL) algorithm for determining the salience of each item in each bag with respect to the bag’s real-valued label. We use an alternating-projections constrained optimization approach to simultaneously learn a regression model and estimate all salience values. We evaluate this algorithm on a significant real-world problem, crop yield modeling, and demonstrate that it provides more extensive, intuitive, and stable salience models than Primary-Instance Regression, which selects a single relevant item from each bag. <s> BIB001 </s> A Review of Multi-Instance Learning Assumptions <s> MI Learning in Other Supervised Settings <s> The multiple-instance learning model has received much attention recently with a primary application area being that of drug activity prediction. Most prior work on multiple-instance learning has been for concept learning, yet for drug activity prediction, the label is a real-valued affinity measurement giving the binding strength. We present extensions of k-nearest neighbors (k-NN), Citation-kNN, and the diverse density algorithm for the real-valued setting and study their performance on Boolean and real-valued data. We also provide a method for generating chemically realistic artificial data. <s> BIB002 </s> A Review of Multi-Instance Learning Assumptions <s> MI Learning in Other Supervised Settings <s> In many data mining applications the data objects are modeled as sets of feature vectors or multi-instance objects. In this paper, we present an expectation maximization approach for clustering multi-instance objects. We therefore present a statistical process that models multi-instance objects. Furthermore, we present M-steps and E-steps for EM clustering and a method for finding a good initial model. In our experimental evaluation, we demonstrate that the new EM algorithm is capable to increase the cluster quality for three real world data sets compared to a κ-medoid clustering. <s> BIB003 </s> A Review of Multi-Instance Learning Assumptions <s> MI Learning in Other Supervised Settings <s> We propose a new problem formulation which is similar to, but more informative than, the binary multiple-instance learning problem. In this setting, we are given groups of instances (described by feature vectors) along with estimates of the fraction of positively-labeled instances per group. The task is to learn an instance level classifier from this information. That is, we are trying to estimate the unknown binary labels of individuals from knowledge of group statistics. We propose a principled probabilistic model to solve this problem that accounts for uncertainty in the parameters and in the unknown individual labels. This model is trained with an efficient MCMC algorithm. Its performance is demonstrated on both synthetic and real-world data arising in general object recognition. <s> BIB004
Although the majority of the research on MI learning has been devoted to classification problems, some work has been done on other supervised MI learning scenarios. The most notable of these are multi-instance multi-label learning  and multi-instance regression BIB001 BIB002 . Similarly to MI classification, any learning approach in these scenarios must depend upon an implicit assumption regarding the nature of the relationship between instances and bag labels; we therefore discuss these assumptions in this section. Other interesting learning scenarios using MI representations include multi-instance clustering (Zhang & Zhou, in press; BIB003 , learning instance-level classifiers from bags labeled with a percentage of positive instances BIB004 , and predicting the salience of instances in an MI regression setting  . None of these scenarios involves bag-level predictions, however, so we do not consider them in this paper.