Multiple-instance learning

Known as: Multiple Instance Learning 
In machine learning, multiple-instance learning (MIL) is a variation on supervised learning. Instead of receiving a set of instances which are… (More)
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Highly Cited
2013
Highly Cited
2013
Saliency detection has been a hot topic in recent years. Its popularity is mainly because of its theoretical meaning for… (More)
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Highly Cited
2010
Highly Cited
2010
Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning… (More)
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Highly Cited
2008
Highly Cited
2008
•Multiple-instance learning (MIL) algorithms train classifiers from lightly supervised data – collections of instances, called… (More)
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Highly Cited
2007
Highly Cited
2007
In this paper we demonstrate how deterministic annealing can be applied to different SVM formulations of the multiple-instance… (More)
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Highly Cited
2006
Highly Cited
2006
Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags… (More)
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Highly Cited
2006
Highly Cited
2006
This paper focuses on kernel methods for multi-instance learning. Existing methods require the prediction of the bag to be… (More)
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2005
2005
We describe a generalization of the multiple-instance learning model in which a bag’s label is not based on a single instance’s… (More)
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Highly Cited
2002
Highly Cited
2002
This paper presents two new formulations of multiple-instance learning as a maximum margin problem. The proposed extensions of… (More)
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Highly Cited
2001
Highly Cited
2001
We present a new multiple-instance (MI) learning technique (EMDD) that combines EM with the diverse density (DD) algorithm. EM-DD… (More)
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Highly Cited
1997
Highly Cited
1997
Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and… (More)
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