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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

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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
2003
Highly Cited
2003
In order to reduce human efforts, there has been increasing interest in applying active learning for training text classifiers… 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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2002
2002
We introduce the Boolean inductive query evaluation problem, which is concerned with answering inductive queries that are… 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
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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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