Hyperrelations in version space
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TL;DR: This paper investigates version space in a hypothesis space where a hypothesis is a hyperrelation, which is in effect a disjunction of conjunctions of disjunctions of attribute-value pairs, and proposes three classification rules for use in different situations based on E-sets.
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About: This article is published in International Journal of Approximate Reasoning. The article was published on 01 Jul 2004. and is currently open access. The article focuses on the topics: Boundary (topology) & Inductive bias.
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Data Decomposition and Decision Rule Joining for Classification of Data with Missing Values
TL;DR: A method is described, called D 3 RJ, that performs data decomposition and decision rule joining to avoid the necessity of reasoning with missing attribute values and it is possible to obtain smaller set of rules and next better classification accuracy than classic decision rule induction methods.
Mass function derivation and combination in multivariate data spaces
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References
•Book
General Lattice Theory
George Grätzer
- 01 Jan 1978
TL;DR: In this paper, the authors define two definitions of Lattices and describe how to describe them, and how to use them to describe lattice geometry, including polynomials, identities, and infinities.
•Proceedings Article
Version spaces: a candidate elimination approach to rule learning
Tom M. Mitchell
- 22 Aug 1977
TL;DR: An approach to this problem is presented which is guaranteed to find, without backtracing, all rule versions consistent with a set of positive and negative training instances.
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
TL;DR: It is shown that the notion of inductive bias in concept learning can be quantified in a way that directly relates to learning performance in the framework recently introduced by Valiant.
Boolean Reasoning for Feature Extraction Problems
Hung Son Nguyen,Andrzej Skowron +1 more
- 15 Oct 1997
TL;DR: An approach based on Boolean reasoning for new feature extraction from data tables with symbolic (nominal, qualitative) attributes is proposed and it is emphasized that Boolean reasoning is a good framework for complexity analysis of the approximate solutions of the discussed problems.