Book Chapter10.1007/3-540-45813-1_31
Concept Learning with Approximation: Rough Version Spaces
Vincent Dubois,Mohamed Quafafou +1 more
19
TL;DR: This paper introduces a rough consistency to rough set theory, and presents a Rough Version Space theory and related methods to address the approximative concept learning problem.
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Abstract: The concept learning problem is a general framework for learning concept consistent with available data. Version Spaces theory and methods are build in this framework. However, it is not designated to handle noisy (possibly inconsistent) data. In this paper, we use rough set theory to improve this framework. Firstly, we introduce a rough consistency. Secondly, we define an approximative concept learning problem. Thirdly, we present a Rough Version Space theory and related methods to address the approximative concept learning problem. Using a didactic example, we put these methods into use. An overview of possible extension of this work concludes this article.
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Citations
Rudiments of rough sets
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Towards a Logic-Based View of Some Approaches to Classification Tasks
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TL;DR: This paper is a plea for revisiting various existing approaches to the handling of data, for classification purposes, based on a set-theoretic view, such as version space learning, formal concept analysis, or analogical proportion-based inference, which rely on different paradigms and motivations and have been developed separately.
References
•Book
Rough Sets: Theoretical Aspects of Reasoning about Data
Zdzisław Pawlak
- 31 Oct 1991
TL;DR: Theoretical Foundations.
8.8K
•Book
Version spaces: an approach to concept learning.
Tom M. Mitchell
- 01 Jan 1979
TL;DR: The version space approach has been implemented as one component of the Meta-DENDRAL program for learning production rules in the domain of chemical spectroscopy and proofs are given for the correctness of the method for representing version spaces, and of the associated concept learning algorithm, for any countably infinite concept description language.
422
•Proceedings Article
Delaying the Choice of Bias: A Disjunctive Version Space Approach.
Michèle Sebag
- 01 Jan 1996
TL;DR: This paper is concerned with alleviating the choice of learning biases via a two-step process: the set of all hypotheses that are consistent with the data and cover at least one training example, is given an implicit characterization of polynomial complexity.
68
•Proceedings Article
Polynomial-time learning with version spaces
Haym Hirsh
- 12 Jul 1992
TL;DR: A new representation for version spaces is presented that is more general than the traditional boundary-set representation, yet has worst-case time complexity that is polynomial in the amount of data when used for learning from attribute-value data with tree-structured feature hierarchies.
43
•Proceedings Article
Version spaces without boundary sets
Haym Hirsh,Nina Mishra,Leonard Pitt +2 more
- 27 Jul 1997
TL;DR: The equivalence of version-space learning to the consistency problem bridges a gap between empirical and theoretical approaches to machine learning, and broadens the class of problems to which version spaces can be applied to include concept classes where boundary sets can have exponential or infinite size and cases where boundaries are not even well defined.
39
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