Inductive Logic ProgrammingInductive logic programming is a new research area formed at the intersection of machine learning and logic programming. While the influence of logic programming has encouraged the development of strong theoretical foundations, this new area is inheriting its experimental orientation from machine learning. Inductive Logic Programming will be an invaluable text for all students of computer science, machine learning and logic programming at an advanced level. * * Examination of the background to current developments within the area * Identification of the various goals and aspirations for the increasing body of researchers in inductive logic programming * Coverage of induction of first order theories, the application of inductive logic programming and discussion of several logic learning programs * Discussion of the applications of inductive logic programming to qualitative modelling, planning and finite element mesh design |
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Page 52
The next result , which is also achieved using the size of a maximum chain of
constrained atoms , provides a tighter bound on the number of examples needed
for PAC - learning . To present this result , we need the following definition that ...
The next result , which is also achieved using the size of a maximum chain of
constrained atoms , provides a tighter bound on the number of examples needed
for PAC - learning . To present this result , we need the following definition that ...
Page 434
We are currently investigating criteria for guiding non - monotonic specialization
based on Algorithmic Complexity Theory . Lastly , these results are only
indicative of the potential of the techniques applied in Experiment 2 . Much
remains to be ...
We are currently investigating criteria for guiding non - monotonic specialization
based on Algorithmic Complexity Theory . Lastly , these results are only
indicative of the potential of the techniques applied in Experiment 2 . Much
remains to be ...
Page 508
The results were then averaged over the five tests . An early version of FOIL ... 1
gives the results of the experiments with non - noisy data . ... According to the
statistical t - test for dependent samples , this result is significant at the 0 . 5 %
level .
The results were then averaged over the five tests . An early version of FOIL ... 1
gives the results of the experiments with non - noisy data . ... According to the
statistical t - test for dependent samples , this result is significant at the 0 . 5 %
level .
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Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
oor A | 22 |
Copyright | |
26 other sections not shown
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Common terms and phrases
algorithm allows applied approach arguments assume background knowledge base body called CIGOL CLINT complete Computer concept consistent constrained atoms constraint constructed contains correct corresponding covers defined definition derivation described domain theory efficient equivalent examples exists explanation expression extend facts false Figure finite first-order formula function given GOLEM ground head Horn clauses hypothesis implied inductive inference input instances Intelligence introduced inverse knowledge knowledge base language least limit literals Logic Programming Machine Learning method Muggleton negative examples non-monotonic Note occur operator ordinary polynomial positive positive examples possible predicates present problem Proceedings proof properties prove queries reasoning relation replacing representation representative resolution respect restricted result rules saturation sentences similar sorted atoms space specialization specific step structure substitution symbol Theorem theory tree true values variables
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Bibliographic information
| Title | Inductive Logic Programming Issue 38 of A.P.I.C. studies in data processing, ISSN 0067-2483 Issue 38 of APIC Series, Automatic Programming Information Centre Brighton Volume 38 of Automatic Programming Information Centre Brighton: The APIC series |
| Editor | Stephen Muggleton |
| Edition | illustrated |
| Publisher | Academic Press, 1992 |
| Original from | the University of Michigan |
| Digitized | 16 Nov 2007 |
| ISBN | 0125097158, 9780125097154 |
| Length | 565 pages |
| Subjects | › › Computers / Intelligence (AI) & Semantics Computers / Programming / Object Oriented |
| Export Citation | BiBTeX EndNote RefMan |