Peter flach saso dzeroski (11 risultati)

Broschiert. Condizione: Gut. 302 Seiten; Das hier angebotene Buch stammt aus einer teilaufgelösten wissenschaftlichen Bibliothek und trägt die entsprechenden Kennzeichnungen (Rückenschild, Instituts-Stempel.); leichte altersbedingte Anbräunung des Papiers; der Buchzustand ist ansonsten ordentlich und dem Alter entsprechend gut. In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 430.…

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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 Eff. …

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 pp. Englisch.…