Multiple-instance learning: Difference between revisions

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In [[machine learning]], '''Multiplemultiple-instance learning''' (MIL) is a variation on [[supervised learning]]. Instead of receiving a set of instances which are individually labeled positive or negative, the learner receives a set of labeled ''bags'', thateach arecontaining labeledmany positive or negativeinstances. In Eachthe bagsimple containscase manyof instances.multiple-instance The[[binary most common assumption is thatclassification]], a bag ismay be labeled negative if all the instances in it are negative. On the other hand, a bag is labeled positive if there is at least one instance in it which is positive. From a collection of labeled bags, the learner tries to either (i) induce a concept that will label individual instances correctly or (ii) learn how to label bags without inducing the concept.

Take image classification for example in {{harvtxt|Amores|2013}}. Given an image, we want to know its target class based on its visual content. 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'' <math>X = \{X_1,..,X_N\}</math>, where each<math>X_i</math> is the feature vector (called ''instance'') extracted from the corresponding i-th region in the image and N is the total regions (instances) partitioning the image. The bag is labeled ''positive'' ("beach") iff. it contains both "sand" region instance and "water" region instance.

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* Text or document categorization

Numerous researchers have worked on adapting classical classification techniques, such as [[support vector machines]] or [[Boosting (meta-algorithm)|boosting]], to work within the context of multiple-instance learning.

Multiple-instance [[regression analysis|regression]] problems are discussed by

==See also==

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| year= 1998 | pages = 341-349

| work= Proceedings of the Fifteenth International Conference on Machine Learning}}.

*{{cite conference

|last1=Ray |first1=Soumya

|first2=David |last2=Page

|title=Multiple instance regression

|conference=ICML

|year=2001

|url=http://pages.cs.wisc.edu/~sray/papers/mip.reg.icml01.pdf}}.

[[Category:Machine learning]]