Multi-instance learning using recurrent neural networks

Multiple instance learning is an increasingly important area in machine learning. In multi-instance learning, the training set is structured into subsets (or bags) of instances. The bags are labelled, but the label of each instance is unknown or irrelevant. In this paper, we revisit the connectionist approach to multi-instance learning. We propose a recurrent neural network model for multi-instance learning. We have applied the new model to a benchmark multi-instance dataset. The results provide evidence that connectionist multi-instance learning is more promising than previously anticipated. We argue that a principled connectionist approach should provide robust and efficient multi-instance learning, yet comparative results should be taken with caution as a result of varying methodologies.