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Multiple instance learning

Depending on the type and variation in training data, machine learning can be roughly categorized into three frameworks: supervised learning… 
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Related topics

Related topics

6 relations
Binary classificationBoosting (machine learning)Multi-label classificationMultiple-instance learning

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2017
2017
Binary fuzzy measures and Choquet integration for multi-source fusion
Countless challenges in engineering require the intelligent combining (aka fusion) of data or information from multiple sources… 
2014
2014
Learning the object location, scale and view for image categorization with adapted classifier
2010
2010
Multiple instance learning for hyperspectral image analysis
Multiple instance learning is a recently researched learning paradigm that allows a machine learning algorithm to learn target… 
2010
2010
A multiple instance learning based framework for semantic image segmentation
Most image segmentation algorithms extract regions satisfying visual uniformity criteria. Unfortunately, because of the semantic… 
2010
2010
Inverse Multiple Instance Learning for Classifier Grids
Recently, classifier grids have shown to be a considerable alternative for object detection from static cameras. However, one… 
2010
2010
An Improved Multiple Instance Learning Algorithm for Object Extraction
NA 
2009
2009
Multiple Instance Learning with Query Bags
In many machine learning applications, precisely labeled data is either burdensome or impossible to collect. Multiple Instance… 
2005
2005
Region-Based Image Clustering and Retrieval Using Multiple Instance Learning
Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years… 
2004
2004
Multiple instance learning using simple classifiers
  • Adam CannonD. Hush
  • International Conference on Machine Learning and…
  • 2004
  • Corpus ID: 29732272
In this paper we study Multiple Instance Learning, a variant of the standard classification problem. We demonstrate the utility… 
2002
2002
User Concept Pattern Discovery Using Relevance Feedback And Multiple Instance Learning For Content-Based Image Retrieval
Understanding and learning the subjective aspect of humans in Content-Based Image Retrieval has been an active research field… 
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