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001 | 978-3-540-48751-7 | ||
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008 | 121227s1999 gw | s |||| 0|eng d | ||
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_a9783540487517 _9978-3-540-48751-7 |
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024 | 7 |
_a10.1007/3-540-48751-4 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
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_aInductive Logic Programming _h[electronic resource] : _b9th International Workshop, ILP-99, Bled, Slovenia, June 24-27, 1999, Proceedings / _cedited by Saso Dzeroski, Peter A. Flach. |
250 | _a1st ed. 1999. | ||
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c1999. |
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300 |
_aVIII, 312 p. _bonline resource. |
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_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v1634 |
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505 | 0 | _aI Invited Papers -- Probabilistic Relational Models -- Inductive Databases -- Some Elements of Machine Learning -- II Contributed Papers -- Refinement Operators Can Be (Weakly) Perfect -- Combining Divide-and-Conquer and Separate-and-Conquer for Efficient and Effective Rule Induction -- Refining Complete Hypotheses in ILP -- Acquiring Graphic Design Knowledge with Nonmonotonic Inductive Learning -- Morphosyntactic Tagging of Slovene Using Progol -- Experiments in Predicting Biodegradability -- 1BC: A First-Order Bayesian Classifier -- Sorted Downward Refinement: Building Background Knowledge into a Refinement Operator for Inductive Logic Programming -- A Strong Complete Schema for Inductive Functional Logic Programming -- Application of Different Learning Methods to Hungarian Part-of-Speech Tagging -- Combining LAPIS and WordNet for the Learning of LR Parsers with Optimal Semantic Constraints -- Learning Word Segmentation Rules for Tag Prediction -- Approximate ILP Rules by Backpropagation Neural Network: A Result on Thai Character Recognition -- Rule Evaluation Measures: A Unifying View -- Improving Part of Speech Disambiguation Rules by Adding Linguistic Knowledge -- On Sufficient Conditions for Learnability of Logic Programs from Positive Data -- A Bounded Search Space of Clausal Theories -- Discovering New Knowledge from Graph Data Using Inductive Logic Programming -- Analogical Prediction -- Generalizing Refinement Operators to Learn Prenex Conjunctive Normal Forms -- Theory Recovery -- Instance based function learning -- Some Properties of Inverse Resolution in Normal Logic Programs -- An Assessment of ILP-assisted models for toxicology and the PTE-3 experiment. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aSoftware engineering. | |
650 | 0 | _aComputer programming. | |
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aSoftware Engineering. |
650 | 2 | 4 | _aProgramming Techniques. |
700 | 1 |
_aDzeroski, Saso. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aFlach, Peter A. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783540661092 |
776 | 0 | 8 |
_iPrinted edition: _z9783662186336 |
830 | 0 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v1634 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/3-540-48751-4 |
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