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Learning First-Order Definitions of Functions
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TLDR
In this paper, a particular first-order learning system is modified to customize it for finding definitions of functional relations, which leads to faster learning times and, in some cases, to definitions that have higher predictive accuracy.Abstract:
First-order learning involves finding a clause-form definition of a relation from examples of the relation and relevant background information. In this paper, a particular first-order learning system is modified to customize it for finding definitions of functional relations. This restriction leads to faster learning times and, in some cases, to definitions that have higher predictive accuracy. Other first-order learning systems might benefit from similar specialization.read more
Citations
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Rule discovery : error measures and conditional rule probability
TL;DR: Various error measures are compared, including average error, mean square error, probability difference and prediction factor, to evaluate their appropriateness for rule discovery and a method of estimating conditional probabilities for a single rule and as a novelty, for rule sets is studied.
Journal Article
Applying machine learning techniques to rule generation in intelligent tutoring systems
TL;DR: This automated rule generation allows generalized rules with a small number of sub-operations to be generated in a reasonable amount of time, and provides non-programmer domain experts with a tool for developing Intelligent Tutoring Systems.
Posted Content
A Comparative Study of the Application of Different Learning Techniques to Natural Language Interfaces
TL;DR: In this paper, a machine learning module replaces an elaborate semantic analysis component to learn the correct mapping of a user's input to the corresponding database command based on a collection of past input data.
Fouille de données relationnelles dans les SGBD.
Cédric Udréa,Fadila Bentayeb +1 more
TL;DR: Un nouvelle approche apporte une solution efficace à la fouille de données relationnelles en intégrant les algorithmes de fouille, en particulier the algorithmes of construction d’arbres de décision, au sein des Systèmes de Gestion de Bases de Données (SGBD).
An M-Health Tool for Improved Self-Care of Heart Failure Patients: An On-Going Field Study
TL;DR: The foundations of the tool to nurture appropriate self-care behaviors along with the theoretical development to serve as the foundation are described, guided by the hypothesis that the mHealth tool will impact the health care provider by reducing emergency department visits and same cause readmissions.
References
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Book
Prolog Programming for Artificial Intelligence
TL;DR: The new edition of Prolog Guide to AI programming has been fully revised and extended to provide an even greater range of applications, enhancing its value as a stand-alone guide to Prolog, artificial intelligence, or AI programming.
Proceedings Article
Efficient Induction of Logic Programs
Stephen Muggleton,C. Feng +1 more
TL;DR: The concept of h-easy rlgg clauses is introduced and it is proved that the length of a certain class of \determinate" r lgg is bounded by a polynomial function of certain features of the background knowledge.
Journal ArticleDOI
Top-down induction of first-order logical decision trees
Hendrik Blockeel,Luc De Raedt +1 more
TL;DR: This dissertation discusses the application domain of decision tree learning and extends it towards the first order logic context of Inductive Logic Programming.
Book ChapterDOI
FOIL: A Midterm Report
TL;DR: This paper summarises the development of FOIL from 1989 up to early 1993 and evaluates its effectiveness on a non-trivial sequence of learning tasks taken from a Prolog programming text.