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181201 ||| eng |
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|a 9783540487517
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|a Dzeroski, Saso
|e [editor]
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| 245 |
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|a Inductive Logic Programming
|h Elektronische Ressource
|b 9th International Workshop, ILP-99, Bled, Slovenia, June 24-27, 1999, Proceedings
|c edited by Saso Dzeroski, Peter A. Flach
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| 250 |
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|a 1st ed. 1999
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 1999, 1999
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| 300 |
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|a VIII, 312 p
|b online resource
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|a I 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
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|a Software engineering
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|a Programming Techniques
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|a Computer programming
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|a Software Engineering
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Artificial intelligence
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| 700 |
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|a Flach, Peter A.
|e [editor]
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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|a Lecture Notes in Artificial Intelligence
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|a 10.1007/3-540-48751-4
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| 856 |
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|u https://doi.org/10.1007/3-540-48751-4?nosfx=y
|x Verlag
|3 Volltext
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|a 006.3
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