Preferred Label : Multiple-Instance Learning Algorithms;
MeSH definition : Weakly supervised learning algorithms where training set is arranged in a labeled
bag of instances, and a single class label is assigned to a bag.;
Définition CISMeF : 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. see https://en.wikipedia.org/wiki/Multiple_instance_learning;
MeSH synonym : Algorithm, Multiple-Instance Learning; Learning Algorithm, Multiple-Instance; Multiple-Instance Learning Algorithm; Multiple-Instance Learning; Learning, Multiple-Instance; Multiple-Instance Learnings; Multiple Instance Learning Algorithms; Multiple Instance Learning; Learning, Multiple Instance; Multiple Instance Learnings;
CISMeF acronym : MIL;
Origin ID : D000098451;
UMLS CUI : C5940551;
Record concept(s)
- Semantic type(s)
Weakly supervised learning algorithms where training set is arranged in a labeled
bag of instances, and a single class label is assigned to a bag.
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. see https://en.wikipedia.org/wiki/Multiple_instance_learning
