About: Multiple instance learning

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In machine learning, multiple-instance learning (MIL) is a type of supervised learning. Instead of receiving a set of instances which are individually labeled, the learner receives a set of labeled bags, each containing many instances. In the simple case of multiple-instance binary classification, a bag may 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.

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rdfs:label	Multiple instance learning (en)
rdfs:comment	In machine learning, multiple-instance learning (MIL) is a type of supervised learning. Instead of receiving a set of instances which are individually labeled, the learner receives a set of labeled bags, each containing many instances. In the simple case of multiple-instance binary classification, a bag may 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. (en)
dbp:wikiPageUsesTemplate	dbt:Cite_book dbt:Cite_conference dbt:Cite_journal dbt:Harvtxt dbt:Reflist dbt:Machine_learning_bar
Subject	Machine learning
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Multiple_instance_learning?oldid=1053483945&ns=0
Wikipage page ID	48841414 (xsd:integer)
page length (characters) of wiki page	34097 (xsd:nonNegativeInteger)
Wikipage revision ID	1053483945 (xsd:integer)
Link from a Wikipage to another Wikipage	Machine learning Supervised learning Support-vector machine Axis-aligned object Binary classification Boosting (machine learning) Calmodulin Machine learning Decision tree Feature (machine learning) Multi-label classification Neural network
has abstract	In machine learning, multiple-instance learning (MIL) is a type of supervised learning. Instead of receiving a set of instances which are individually labeled, the learner receives a set of labeled bags, each containing many instances. In the simple case of multiple-instance binary classification, a bag may 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. Babenko (2008) gives a simple example for MIL. Imagine several people, and each of them has a key chain that contains few keys. Some of these people are able to enter a certain room, and some aren’t. The task is then to predict whether a certain key or a certain key chain can get you into that room. To solve this problem we need to find the exact key that is common for all the “positive” key chains. If we can correctly identify this key, we can also correctly classify an entire key chain - positive if it contains the required key, or negative if it doesn't. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Multiple_instance_learning
is Wikipage redirect of	Multiple-instance learning Multiple Instance Learning Algorithms for multiple instance learning
is Link from a Wikipage to another Wikipage of	Alternative splicing Josef Kittler Document classification Content-based image retrieval Kernel embedding of distributions Manifold alignment Multi-label classification Multiple-instance learning Multiple Instance Learning Algorithms for multiple instance learning
is foaf:primaryTopic of	http://en.wikipedia.org/wiki/Multiple_instance_learning

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