Problem of multi-instance learning is not unique to drug finding.In 1998, Maron and Ratan found another application of multiple instance learning to scene classification in machine vision, and devised Diverse Density framework.If the space of instances is make the assumption regarding the relationship between the instances within a bag and the class label of the bag.Tags: Ama Style Term PapersReflections EssaysBatting Cage Business PlanBalance Trade Research PaperEssays In English For Primary ClassesHow An Essay Should Look LikeFall Of The Roman Empire EssaysCollaborative Problem Solving ModelNature In English Poetry EssayWrite An Essay About Your First Day In Secondary School
Multiple instance learning (MIL) falls under the supervised learning framework, where every training instance has a label, either discrete or real valued.
MIL deals with problems with incomplete knowledge of labels in training sets.
Multiple instance learning can be used to learn the properties of the subimages which characterize the target scene.
From there on, these frameworks have been applied to a wide spectrum of applications, ranging from image concept learning and text categorization, to stock market prediction. Amores (2013) Given an image, we want to know its target class based on its visual content.
They tried to create a learning systems that could predict whether new molecule was qualified to make some drug, or not, through analyzing a collection of known molecules.
Molecules can have many alternative low-energy states, but only one, or some of them, are qualified to make a drug.
For instance, the target class might be "beach", where the image contains both "sand" and "water".
In MIL terms, the image is described as a bag is the total regions (instances) partitioning the image.
The bag is labeled positive ("beach") if it contains both "sand" region instances and "water" region instances.
Examples of where MIL is applied are: Numerous researchers have worked on adapting classical classification techniques, such as support vector machines or boosting, to work within the context of multiple-instance learning.