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Version space learning

Known as: Candidate elimination, Version Space, Version spaces 
Version space learning is a logical approach to machine learning, specifically binary classification. Version space learning algorithms search a… Expand
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2004
Highly Cited
2004
Programming by demonstration enables users to easily personalize their applications, automating repetitive tasks simply by… Expand
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Highly Cited
2004
Highly Cited
2004
The problem of scarcity of labeled pixels, required for segmentation of remotely sensed satellite images in supervised pixel… Expand
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Review
2003
Review
2003
We report the final results of the search for gravitationally lensed flat-spectrum radio sources found in the combination of… Expand
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2002
2002
Version space is used in inductive concept learning to represent the hypothesis space where the goal concept is expressed as a… Expand
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Highly Cited
2001
Highly Cited
2001
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other… Expand
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Highly Cited
2001
Highly Cited
2001
Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine… Expand
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Highly Cited
2001
Highly Cited
2001
We present the application of Feature Mining techniques to the Developmental Therapeutics Program's AIDS antiviral screen… Expand
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Highly Cited
1999
Highly Cited
1999
From a Bayesian perspective Support Vector Machines choose the hypothesis corresponding to the largest possible hypersphere that… Expand
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Highly Cited
1992
Highly Cited
1992
Although version spaces provide a useful conceptual tool for inductive concept learning, they often face severe computational… Expand
Highly Cited
1977
Highly Cited
1977
An important research problem in artificial intelligence is the study of methods for learning general concepts or rules from a… Expand
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