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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
Wikipedia

Related topics

Related topics

7 relations
Binary classificationFormal concept analysisInductive logic programmingRough set
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Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2004
Highly Cited
2004
Programming by Demonstration Using Version Space Algebra
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
Representative Sampling for Text Classification Using Support Vector Machines
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 Learning with DNA Molecules
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
A theory of inductive query answering
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
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
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 Machine Active Learning with Applications to Text Classification
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
Molecular feature mining in HIV data
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
Bayes Point Machines: Estimating the Bayes Point in Kernel Space
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
Polynomial-Time Learning with Version Spaces
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
Version Spaces: A Candidate Elimination Approach to Rule Learning
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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