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Inductive Logic Programming : 9th International Workshop, Ilp-99, Bled, Slovenia, June 24-27, 1999, Proceedings - Saso Dzeroski

Inductive Logic Programming

9th International Workshop, Ilp-99, Bled, Slovenia, June 24-27, 1999, Proceedings

By: Saso Dzeroski (Editor), Peter Flach (Editor)

Paperback Published: 9th June 1999
ISBN: 9783540661092
Number Of Pages: 312

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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

Invited Papers
Probabilistic Relational Modelsp. 3
Inductive Databases (Abstract)p. 14
Some Elements of Machine Learning (Extended Abstract)p. 15
Contributed Papers
Refinement Operators Can Be (Weakly) Perfectp. 21
Combining Divide-and-Conquer and Separate-and-Conquer for Efficient and Effective Rule Inductionp. 33
Refining Complete Hypotheses in ILPp. 44
Acquiring Graphic Design Knowledge with Nonmonotonic Inductive Learningp. 56
Morphosyntactic Tagging of Slovene Using Progolp. 68
Experiments in Predicting Biodegradabilityp. 80
1BC: A First-Order Bayesian Classifierp. 92
Sorted Downward Refinement: Building Background Knowledge into a Refinement Operator for Inductive Logic Programmingp. 104
A Strong Complete Schema for Inductive Functional Logic Programmingp. 116
Application of Different Learning Methods to Hungarian Part-of-Speech Taggingp. 128
Combining LAPIS and WordNet for the Learning of LR Parsers with Optimal Semantic Constraintsp. 140
Learning Word Segmentation Rules for Tag Predictionp. 152
Approximate ILP Rules by Backpropagation Neural Network: A Result on Thai Character Recognitionp. 162
Rule Evaluation Measures: A Unifying Viewp. 174
Improving Part-of-Speech Disambiguation Rules by Adding Linguistic Knowledgep. 186
On Sufficient Conditions for Learnability of Logic Programs from Positive Datap. 198
A Bounded Search Space of Clausal Theoriesp. 210
Discovering New Knowledge from Graph Data Using Inductive Logic Programmingp. 222
Analogical Predictionp. 234
Generalizing Refinement Operators to Learn Prenex Conjunctive Normal Formsp. 245
Theory Recoveryp. 257
Instance Based Function Learningp. 268
Some Properties of Inverse Resolution in Normal Logic Programsp. 279
An Assessment of ILP-Assisted Models for Toxicology and the PTE-3 Experimentp. 291
Author Indexp. 303
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9783540661092
ISBN-10: 3540661093
Series: Lecture Notes in Computer Science
Audience: General
Format: Paperback
Language: English
Number Of Pages: 312
Published: 9th June 1999
Publisher: SPRINGER VERLAG GMBH
Country of Publication: DE
Dimensions (cm): 23.39 x 15.6  x 1.7
Weight (kg): 0.45