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Stochastic propositionalization of non-determinate background knowledge

https://doi.org/10.1007/bfb0027312
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Feature construction with inductive logic programming: A study of quantitative predictions of biological activity by structural attributes
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The Minimum Description Length Principle and Categorical Theories
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Learning Boolean Functions in an Infinite Attribute Space
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Prediction of rodent carcinogenicity bioassays from molecular structure using inductive logic programming.
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Substructure Discovery Using Minimum Description Length and Background Knowledge
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Prediction of Rodent Carcinogenicity Bioassays from Molecular Structure Using Inductive Logic Programming
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Low Size-Complexity Inductive Logic Programming: The East-West Challenge Considered as a Problem in Cost-Sensitive Classification

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Mining Relational Association Rules for Propositional Classification
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Support Vector Inductive Logic Programming
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Support Vector Inductive Logic Programming
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Propositionalisation and Aggregates
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Transformation-Based Learning Using Multirelational Aggregation
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The Representation Race — Preprocessing for Handling Time Phenomena
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Weight Your Words: The Effect of Different Weighting Schemes on Wordification Performance
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Propositionalization of Relational Data
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World Cup of Flags: A Multi-relational Competitive Vexillology Dataset from Twitter
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Macro-Operators Revisited in Inductive Logic Programming
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Approximate Relational Reasoning by Stochastic Propositionalization
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A Wordification Approach to Relational Data Mining
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Propositionalization Approaches to Relational Data Mining
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Learning structurally indeterminate clauses
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Propositionalization-based relational subgroup discovery with RSD
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A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data
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Wordification: Propositionalization by unfolding relational data into bags of words
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Feature construction with Inductive Logic Programming: A Study of Quantitative Predictions of Biological Activity Aided by Structural Attributes
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An extended transformation approach to inductive logic programming
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Learning Comprehensible Theories from Structured Data
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Explainable Classification and Annotation through Relation Learning and Reasoning
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