Neural networks for multi-instance learning

Multi-instance learning originates from the investigation on drug activity prediction, where the task is to predict whether an unseen molecule could be used to make some drug. Such a problem is difficult because a molecule may have many alternative shapes with low energy, yet only one of those shapes may be responsible for the qualification of the molecule to make the drug. Because of its unique characteristics and extensive existence, multi-instance learning is regarded as a new machine learning framework parallel to supervised learning, unsupervised learning, and reinforcement learning. In this paper, an open problem of this area is addressed. That is, a popular neural network algorithm is adapted for multi-instance learning through employing a specific error function. Experiments show that the adapted algorithm achieves good result on the drug activity prediction data. 1