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

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

Paperback Lecture Notes in Computer Science

Edited by Saso Dzeroski, Edited by Peter Flach

USD$98.99

Free delivery worldwide
Available
Dispatched in 3 business days
When will my order arrive?

  • Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Format: Paperback | 312 pages
  • Dimensions: 155mm x 236mm x 15mm | 386g
  • Publication date: 9 June 1999
  • Publication City/Country: Berlin
  • ISBN 10: 3540661093
  • ISBN 13: 9783540661092
  • Edition statement: 1999 ed.
  • Illustrations note: biography

Product description

Thisvolumecontains3invitedand24submittedpaperspresentedattheNinth InternationalWorkshoponInductiveLogicProgramming,ILP-99. The24acc- tedpaperswereselectedbytheprogramcommitteefromthe40paperssubmitted toILP-99. Eachpaperwasreviewedbythreereferees,applyinghighreviewing standards. ILP-99washeldinBled,Slovenia,24{27June1999. Itwascollocatedwith theSixteenthInternationalConferenceonMachineLearning,ICML-99,held27{ 30June1999. On27June,ILP-99andICML-99weregivenajointinvitedtalk byJ. RossQuinlanandajointpostersessionwhereallthepapersacceptedat ILP-99andICML-99werepresented. TheproceedingsofICML-99(editedby IvanBratkoandSa soD zeroski)arepublishedbyMorganKaufmann. WewishtothankalltheauthorswhosubmittedtheirpaperstoILP-99,the programcommitteemembersandotherreviewersfortheirhelpinselectinga high-qualityprogram,andtheinvitedspeakers:DaphneKoller,HeikkiMannila, andJ. RossQuinlan. ThanksareduetoTanjaUrban ci candherteamandMajda Zidanskiandherteamfortheorganizationalsupportprovided. Wewishtothank AlfredHofmannandAnnaKramerofSpringer-Verlagfortheircooperationin publishing these proceedings. Finally, we gratefully acknowledge the nancial supportprovidedbythesponsorsofILP-99. April1999 Sa soD zeroski PeterFlach ILP-99ProgramCommittee FrancescoBergadano(UniversityofTorino) HenrikBostr..om(UniversityofStockholm) IvanBratko(UniversityofLjubljana) WilliamCohen(AT&TResearchLabs) JamesCussens(UniversityofYork) LucDeRaedt(UniversityofLeuven) Sa soD zeroski(Jo zefStefanInstitute,co-chair) PeterFlach(UniversityofBristol,co-chair) AlanFrisch(UniversityofYork) KoichiFurukawa(KeioUniversity) RoniKhardon(UniversityofEdinburgh) NadaLavra c(Jo zefStefanInstitute) JohnLloyd(AustralianNationalUniversity) StanMatwin(UniversityofOttawa) RaymondMooney(UniversityofTexas) StephenMuggleton(UniversityofYork) 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 ...1 28 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 ...186 OnSu cientConditionsforLearnabilityofLogicProgramsfrom PositiveData E. Martin,A. Sharma ...198 ABoundedSearchSpaceofClausalTheories H. Midelfart...210 DiscoveringNewKnowledgefromGraphData UsingInductiveLogicProgramming T. Miyahara,T. Shoudai,T. Uchida,T. Kuboyama, K. Takahashi,H. Ueda...

Other people who viewed this bought:

Showing items 1 to 10 of 10

Other books in this category

Showing items 1 to 11 of 11
Categories:

Table of contents

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.