000 | 03384nam a22005895i 4500 | ||
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001 | 978-3-540-69049-8 | ||
003 | DE-He213 | ||
005 | 20240423132531.0 | ||
007 | cr nn 008mamaa | ||
008 | 121227s1997 gw | s |||| 0|eng d | ||
020 |
_a9783540690498 _9978-3-540-69049-8 |
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024 | 7 |
_a10.1007/3-540-62927-0 _2doi |
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050 | 4 | _aQA76.758 | |
072 | 7 |
_aUMZ _2bicssc |
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_aCOM051230 _2bisacsh |
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072 | 7 |
_aUMZ _2thema |
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082 | 0 | 4 |
_a005.1 _223 |
100 | 1 |
_aNienhuys-Cheng, Shan-Hwei. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aFoundations of Inductive Logic Programming _h[electronic resource] / _cby Shan-Hwei Nienhuys-Cheng, Ronald de Wolf. |
250 | _a1st ed. 1997. | ||
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c1997. |
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300 |
_aXVIII, 410 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v1228 |
|
505 | 0 | _aPropositional logic -- First-order logic -- Normal forms and Herbrand models -- Resolution -- Subsumption theorem and refutation completeness -- Linear and input resolution -- SLD-resolution -- SLDNF-resolution -- What is inductive logic programming? -- The framework for model inference -- Inverse resolution -- Unfolding -- The lattice and cover structure of atoms -- The subsumption order -- The implication order -- Background knowledge -- Refinement operators -- PAC learning -- Further topics. | |
520 | _aInductive Logic Programming is a young and rapidly growing field combining machine learning and logic programming. This self-contained tutorial is the first theoretical introduction to ILP; it provides the reader with a rigorous and sufficiently broad basis for future research in the area. In the first part, a thorough treatment of first-order logic, resolution-based theorem proving, and logic programming is given. The second part introduces the main concepts of ILP and systematically develops the most important results on model inference, inverse resolution, unfolding, refinement operators, least generalizations, and ways to deal with background knowledge. Furthermore, the authors give an overview of PAC learning results in ILP and of some of the most relevant implemented systems. | ||
650 | 0 | _aSoftware engineering. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aMachine theory. | |
650 | 0 | _aComputer programming. | |
650 | 1 | 4 | _aSoftware Engineering. |
650 | 2 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aFormal Languages and Automata Theory. |
650 | 2 | 4 | _aProgramming Techniques. |
700 | 1 |
_aWolf, Ronald de. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783540629276 |
776 | 0 | 8 |
_iPrinted edition: _z9783662174852 |
830 | 0 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v1228 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/3-540-62927-0 |
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999 |
_c188814 _d188814 |