Iterative version spaces
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TL;DR: An incremental depth-first algorithm for computing the S- and ς-set of Mitchell's Candidate Elimination and Mellish's Description Identification algorithm is presented, with the result that the worst-case space complexity of the algorithm is linear in the number of lower- and upperbounds.
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About: This article is published in Artificial Intelligence. The article was published on 01 Sep 1994. and is currently open access. The article focuses on the topics: Time complexity.
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Citations
Database Support for Data Mining Applications
Rosa Meo,Pier Luca Lanzi,Mika Klemettinen +2 more
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TL;DR: This work presents a synthetic view on important concepts that have been studied within the cInQ European project when considering the pattern domain of itemsets, and introduces the concepts of pattern domain, evaluation functions, primitive constraints, inductive queries and solvers for itemsets.
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Query Languages Supporting Descriptive Rule Mining: A Comparative Study
TL;DR: A comparison between three query languages (MSQL, DMQL and MINE RULE) that have been proposed for descriptive rule mining and discussed their common features and differences are provided.
Concept Learning with Approximation: Rough Version Spaces
Vincent Dubois,Mohamed Quafafou +1 more
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19
Data Mining as Constraint Logic Programming
Luc De Raedt
- 01 Jan 2002
TL;DR: The semantics of RDM is defined, the semantics of a solver is presented and the resulting query language allows us to declaratively specify the patterns of interest, the solver then takes care of the procedural aspects.
Inductive Logic Programming: A Survey of European Research
Nada Lavrač,Luc De Raedt +1 more
TL;DR: The paper gives a survey of European research in this area and briefly introduces the research field, then gives an overview of recent research results and directions and describes in more detail the research of some of the European institutions involved in ILP research within two European ILP projects.
10
References
Inductive Logic Programming : Theory and Methods
Stephen Muggleton,Luc De Raedt +1 more
TL;DR: The most important theories and methods of Inductive Logic Programming, a new discipline which investigates the inductive construction of first-order clausal theories from examples and background knowledge, are surveyed.
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Depth-first iterative-deepening: an optimal admissible tree search
TL;DR: This heuristic depth-first iterative-deepening algorithm is the only known algorithm that is capable of finding optimal solutions to randomly generated instances of the Fifteen Puzzle within practical resource limits.
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Generalization as search
TL;DR: The problem of concept learning, or forming a general description of a class of objects given a set of examples and non-examples, is viewed here as a search problem.
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Depth-first iterative-deepening: an optimal admissible tree search
TL;DR: A depth·first iterative·deepening algorithm is shown to be asymptotically optimal along all three dimmsions for exponential tree searches and is the only known algorithm that is capable of finding optimal solUtions to randomly generated instances of the Fif/un Puzzle within practical resource.
Learning concepts by asking questions
Claude Sammut,Ranan B. Banerji +1 more
- 01 Jan 1998
TL;DR: This chapter describes a program, called Marvin, which uses concepts it has learned previously to learn new concepts, and forms hypotheses about the concept being learned and tests the hypotheses by asking the trainer questions.