Skip to search formSkip to main contentSkip to account menu

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… 
Wikipedia (opens in a new tab)

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2010
2010
We present a formal approach for combining programming by demonstration (PbD) with programming by instruction (PbI)--a largely… 
Review
2006
Review
2006
This paper surveys various areas in information engineering where an explicit handling of positive and negative sides of… 
2005
2005
In this paper we prove that the reliability of the classifications of individual instances, provided by a classifier, results in… 
2003
2003
In this paper we propose a lattice-based approach intended for extracting semantics from datacubes: borders of version spaces for… 
2002
2002
This position paper presents a simple triad model for generating communicating systems. We show a simple architectural and… 
2001
2001
This article presents a learning agent shell and methodology for building knowledge bases and agents and their innovative… 
2001
2001
Lazy Bayesian Rules modifies naive Bayesian classification to undo elements of the harmful attribute independence assumption. It… 
2000
2000
This paper presents an investigation of the numerical computations of nonweak/strong solutions of linear and nonlinear hyperbolic… 
1995
1995
There is not yet a common agreement on basic versioning models. Tichy [Tic88] distinguishes between sequential revisions and… 
1992
1992
It is well{recognized that in practical inductive learning systems the search for a concept must be heavily biased. In addition…