Riassunto:
Shan-HweiNienhuys-Cheng(UniversityofRotterdam) DavidPage(UniversityofLouisville) BernhardPfahringer(AustrianResearchInstituteforAI) CelineRouveirol(UniversityofParis) ClaudeSammut(UniversityofNewSouthWales) MicheleSebag(EcolePolytechnique) AshwinSrinivasan(UniversityofOxford) PrasadTadepalli(OregonStateUniversity) StefanWrobel(GMDResearchCenterforInformationTechnology) OrganizationalSupport TheAlbatrossCongressTouristAgency,Bled Center for Knowledge Transfer in Information Technologies, Jo zef Stefan Institute,Ljubljana SponsorsofILP-99 ILPnet2,NetworkofExcellenceinInductiveLogicProgramming COMPULOGNet,EuropeanNetworkofExcellenceinComputationalLogic Jo zefStefanInstitute,Ljubljana LPASoftware,Inc. UniversityofBristol TableofContents I InvitedPapers ProbabilisticRelationalModels D. Koller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 InductiveDatabases(Abstract) H. Mannila. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 SomeElementsofMachineLearning(ExtendedAbstract) J. R. Quinlan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 II ContributedPapers Re nementOperatorsCanBe(Weakly)Perfect L. Badea,M. Stanciu. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 CombiningDivide-and-ConquerandSeparate-and-ConquerforE cientand E ectiveRuleInduction H. Bostr¨om,L. Asker. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Re ningCompleteHypothesesinILP I. Bratko. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 AcquiringGraphicDesignKnowledge withNonmonotonicInductiveLearning K. Chiba,H. Ohwada,F. Mizoguchi. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 MorphosyntacticTaggingofSloveneUsingProgol J. Cussens,S. D zeroski,T. Erjavec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 ExperimentsinPredictingBiodegradability S. D zeroski,H. Blockeel,B. Kompare,S. Kramer, B. Pfahringer,W. VanLaer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 1BC:AFirst-OrderBayesianClassi er P. Flach,N. Lachiche. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 SortedDownwardRe nement:BuildingBackgroundKnowledge intoaRe nementOperatorforInductiveLogicProgramming A. M. Frisch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 AStrongCompleteSchemaforInductiveFunctionalLogicProgramming J. Hern andez-Orallo,M. J. Ram rez-Quintana. . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 ApplicationofDi erentLearningMethods toHungarianPart-of-SpeechTagging T. Horv ath,Z. Alexin,T. Gyim othy,S. Wrobel . . . . . . . . . . . . . . . . . . . . . . . . . . 128 VIII TableofContents CombiningLAPISandWordNetfortheLearningofLRParserswith OptimalSemanticConstraints D. Kazakov. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 LearningWordSegmentationRulesforTagPrediction D. Kazakov,S. Manandhar,T. Erjavec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 ApproximateILPRulesbyBackpropagationNeuralNetwork: AResultonThaiCharacterRecognition B. Kijsirikul,S. Sinthupinyo. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 RuleEvaluationMeasures:AUnifyingView N. Lavra c,P. Flach,B. Zupan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 ImprovingPart-of-SpeechDisambiguationRulesbyAdding LinguisticKnowledge N. Lindberg,M. Eineborg
Contenuti:
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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