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 |
From inside the book
Results 1-3 of 84
Page 12
4 PAC - learning One popular machine learning approach to the problem of
constructing highly probable hypotheses is ... of hypotheses is PAC - learnable
whenever we can guarantee with high probability that an arbitrarily chosen
hypothesis ...
4 PAC - learning One popular machine learning approach to the problem of
constructing highly probable hypotheses is ... of hypotheses is PAC - learnable
whenever we can guarantee with high probability that an arbitrarily chosen
hypothesis ...
Page 199
( 1 , 2 , 3 ) = ( 2141 ] Figure 1 : Proving the inconsistency of a completed
hypothesis with an example plete explanation , weak explanation can be useful
even when inducing strong theories . Completeness of strong explanations can
be ...
( 1 , 2 , 3 ) = ( 2141 ] Figure 1 : Proving the inconsistency of a completed
hypothesis with an example plete explanation , weak explanation can be useful
even when inducing strong theories . Completeness of strong explanations can
be ...
Page 345
The hypothesis space , defined by the sets of available rule models and
predicates and the topology , is searched top - down ... The search is along the
generality hierarchy with pruning of specializations of accepted and failed
hypotheses .
The hypothesis space , defined by the sets of available rule models and
predicates and the topology , is searched top - down ... The search is along the
generality hierarchy with pruning of specializations of accepted and failed
hypotheses .
What people are saying - Write a review
We haven't found any reviews in the usual places.
Contents
Inductive Logic Programming | 4 |
A Framework for Inductive Logic Programming | 9 |
oor A | 22 |
Copyright | |
26 other sections not shown
Other editions - View all
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
References to this book
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 / Artificial Intelligence / General Computers / Programming / Object Oriented |
| Export Citation | BiBTeX EndNote RefMan |