In multi-instance learning, the training examples are bags composed of instances without
labels, and the task is to predict the labels of unseen bags through analyzing the training …

Multi-instance learning is regarded as a new learning framework where the training
examples are bags composed of instances without labels, and the task is to predict the …

Multi-instance learning (MIL) has been widely applied to diverse applications involving
complicated data objects, such as images and genes. However, most existing MIL …

In multiple-instance learning (MIL), the existing bag encoding and attention-based pooling
approaches assume that the instances in the bag have no relationship among them. This …

The effectiveness of multi-instance learning (MIL) has been demonstrated by its wide
spectrum of applications in computer vision, biometrics, and natural language processing …

In multi-instance learning, the training set comprises labeled bags that are composed of
unlabeled instances, and the task is to predict the labels of unseen bags. Through analyzing …

Multi-instance learning (MIL) considers input as bags of instances, in which labels are
assigned to the bags. MIL is useful in many real-world applications. For example, in image …

Multi-instance learning was coined by Dietterich et al. in their investigation on drug activity
prediction. In such a learning framework, the training examples are bags composed of …

Abstract Multi-instance learning (MIL), as a special version of classification, focuses on
labeled sets (bags) consisting of unlabeled instances and has drawn accumulative attention …

Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects
(instances), where the individual instance labels are ambiguous. In this setting, supervised …