Inductive Logic Programming

This book constitutes the refereed proceedings of the 15th International Conference on Inductive Logic Programming, ILP 2005, held in Bonn, Germany, in August 2005.

Author: Germany) Ilp 200 (2005 Bonn

Publisher: Springer Science & Business Media

ISBN: 3540281770

Category: Computers

Page: 425

View: 887

This book constitutes the refereed proceedings of the 15th International Conference on Inductive Logic Programming, ILP 2005, held in Bonn, Germany, in August 2005. The 24 revised full papers presented together with the abstract of 4 invited lectures were carefully reviewed and selected for inclusion in the book. The papers address all current topics in inductive logic programming, ranging from theoretical and methodological issues to advanced applications in various areas, also including more diverse forms of non-propositional learning.

Inductive Logic Programming

This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orléans, France, in September 2017.

Author: Nicolas Lachiche

Publisher: Springer

ISBN: 3319780905

Category: Mathematics

Page: 185

View: 830

This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orléans, France, in September 2017. The 12 full papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.

Inductive Logic Programming

This book constitutes the thoroughly refereed post-proceedings of the 22nd International Conference on Inductive Logic Programming, ILP 2012, held in Dubrovnik, Croatia, in September 2012.

Author: Fabrizio Riguzzi

Publisher: Springer

ISBN: 3642388124

Category: Mathematics

Page: 273

View: 848

This book constitutes the thoroughly refereed post-proceedings of the 22nd International Conference on Inductive Logic Programming, ILP 2012, held in Dubrovnik, Croatia, in September 2012. The 18 revised full papers were carefully reviewed and selected from 41 submissions. The papers cover the following topics: propositionalization, logical foundations, implementations, probabilistic ILP, applications in robotics and biology, grammatical inference, spatial learning and graph-based learning.

Inductive Logic Programming

This book constitutes the thoroughly refereed post-conference proceedings of the 24th International Conference on Inductive Logic Programming, ILP 2014, held in Nancy, France, in September 2014.

Author: Jesse Davis

Publisher: Springer

ISBN: 331923708X

Category: Mathematics

Page: 211

View: 570

This book constitutes the thoroughly refereed post-conference proceedings of the 24th International Conference on Inductive Logic Programming, ILP 2014, held in Nancy, France, in September 2014. The 14 revised papers presented were carefully reviewed and selected from 41 submissions. The papers focus on topics such as the inducing of logic programs, learning from data represented with logic, multi-relational machine learning, learning from graphs, and applications of these techniques to important problems in fields like bioinformatics, medicine, and text mining.

Inductive Logic Programming

This book constitutes the thoroughly refereed post-proceedings of the 21st International Conference on Inductive Logic Programming, ILP 2011, held in Windsor Great Park, UK, in July/August 2011.

Author: Stephen Muggleton

Publisher: Springer

ISBN: 3642319513

Category: Computers

Page: 406

View: 748

This book constitutes the thoroughly refereed post-proceedings of the 21st International Conference on Inductive Logic Programming, ILP 2011, held in Windsor Great Park, UK, in July/August 2011. The 24 revised full papers were carefully reviewed and selected from 66 submissions. Also included are five extended abstracts and three invited talks. The papers represent the diversity and vitality in present ILP research including ILP theory, implementations, probabilistic ILP, biological applications, sub-group discovery, grammatical inference, relational kernels, learning of Petri nets, spatial learning, graph-based learning, and learning of action models.

Inductive Logic Programming

This book constitutes the thoroughly refereed post-conference proceedings of the 25th International Conference on Inductive Logic Programming, ILP 2015, held in Kyoto, Japan, in August 2015.

Author: Katsumi Inoue

Publisher: Springer

ISBN: 9783319405650

Category: Mathematics

Page: 215

View: 399

This book constitutes the thoroughly refereed post-conference proceedings of the 25th International Conference on Inductive Logic Programming, ILP 2015, held in Kyoto, Japan, in August 2015. The 14 revised papers presented were carefully reviewed and selected from 44 submissions. The papers focus on topics such as theories, algorithms, representations and languages, systems and applications of ILP, and cover all areas of learning in logic, relational learning, relational data mining, statistical relational learning, multi-relational data mining, relational reinforcement learning, graph mining, connections with other learning paradigms, among others.

Probabilistic Inductive Logic Programming

This book provides an introduction to the field with an emphasis on the methods based on logic programming principles.

Author: Luc De Raedt

Publisher: Springer Science & Business Media

ISBN: 3540786511

Category: Computers

Page: 339

View: 244

The question, how to combine probability and logic with learning, is getting an increased attention in several disciplines such as knowledge representation, reasoning about uncertainty, data mining, and machine learning simulateously. This results in the newly emerging subfield known under the names of statistical relational learning and probabilistic inductive logic programming. This book provides an introduction to the field with an emphasis on the methods based on logic programming principles. It is concerned with formalisms and systems, implementations and applications, as well as with the theory of probabilistic inductive logic programming. The 13 chapters of this state-of-the-art survey start with an introduction to probabilistic inductive logic programming; moreover the book presents a detailed overview of the most important probabilistic logic learning formalisms and systems such as relational sequence learning techniques, using kernels with logical representations, Markov logic, the PRISM system, CLP(BN), Bayesian logic programs, and the independent choice logic. The third part provides a detailed account of some show-case applications of probabilistic inductive logic programming. The final part touches upon some theoretical investigations and includes chapters on behavioural comparison of probabilistic logic programming representations and a model-theoretic expressivity analysis.

Inductive Logic Programming

This book constitutes the thoroughly refereed post-conference proceedings of the 17th International Conference on Inductive Logic Programming, ILP 2007, held in Corvallis, OR, USA, in June 2007 in conjunction with ICML 2007, the ...

Author: Hendrik Blockeel

Publisher: Springer Science & Business Media

ISBN: 3540784683

Category: Computers

Page: 307

View: 149

This book constitutes the thoroughly refereed post-conference proceedings of the 17th International Conference on Inductive Logic Programming, ILP 2007, held in Corvallis, OR, USA, in June 2007 in conjunction with ICML 2007, the International Conference on Machine Learning. The 15 revised full papers and 11 revised short papers presented together with 2 invited lectures were carefully reviewed and selected from 38 initial submissions. The papers present original results on all aspects of learning in logic, as well as multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, and learning in other non-propositional knowledge representation frameworks. Thus all current topics in inductive logic programming, ranging from theoretical and methodological issues to advanced applications in various areas are covered.

Inductive Logic Programming

This book constitutes the refereed proceedings of the 13th International Conference on Inductive Logic Programming, ILP 2003, held in Szeged, Hungary in September/October 2003.

Author: Tamas Horváth

Publisher: Springer Science & Business Media

ISBN: 3540201440

Category: Computers

Page: 400

View: 825

This book constitutes the refereed proceedings of the 13th International Conference on Inductive Logic Programming, ILP 2003, held in Szeged, Hungary in September/October 2003. The 23 revised full papers presented were carefully reviewed and selected from 53 submissions. Among the topics addressed are multirelational data mining, complexity issues, theory revision, clustering, mathematical discovery, relational reinforcement learning, multirelational learning, inductive inference, description logics, grammar systems, and inductive learning.

An Inductive Logic Programming Approach to Statistical Relational Learning

Talks about Logic Programming, Uncertainty Reasoning and Machine Learning. This book includes definitions that circumscribe the area formed by extending Inductive Logic Programming to cases annotated with probability values.

Author: Kristian Kersting

Publisher: IOS Press

ISBN: 9781586036744

Category: Computers

Page: 228

View: 591

Talks about Logic Programming, Uncertainty Reasoning and Machine Learning. This book includes definitions that circumscribe the area formed by extending Inductive Logic Programming to cases annotated with probability values. It investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher Kernels.

Inductive Logic Programming

This book constitutes the thoroughly refereed post-proceedings of the 23rd International Conference on Inductive Logic Programming, ILP 2013, held in Rio de Janeiro, Brazil, in August 2013.

Author: Gerson Zaverucha

Publisher: Springer

ISBN: 3662449234

Category: Mathematics

Page: 141

View: 849

This book constitutes the thoroughly refereed post-proceedings of the 23rd International Conference on Inductive Logic Programming, ILP 2013, held in Rio de Janeiro, Brazil, in August 2013. The 9 revised extended papers were carefully reviewed and selected from 42 submissions. The conference now focuses on all aspects of learning in logic, multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, and other forms of learning from structured data.

Inductive Logic Programming

Inductive logic programming is a new research area formed at the intersection of machine learning and logic programming.

Author: Stephen Muggleton

Publisher: Morgan Kaufmann

ISBN: 9780125097154

Category: Computers

Page: 565

View: 221

Inductive logic programming is a new research area formed at the intersection of machine learning and logic programming. While the influence of logic programming has encouraged the development of strong theoretical foundations, this new area is inheriting its experimental orientation from machine learning. Inductive Logic Programming will be an invaluable text for all students of computer science, machine learning and logic programming at an advanced level. * * Examination of the background to current developments within the area * Identification of the various goals and aspirations for the increasing body of researchers in inductive logic programming * Coverage of induction of first order theories, the application of inductive logic programming and discussion of several logic learning programs * Discussion of the applications of inductive logic programming to qualitative modelling, planning and finite element mesh design

Inductive Logic Programming

This book constitutes the proceedings of the 19th International Conference on Inductive Logic Programming, held in Leuven, Belgium, in July 2009.

Author: Luc De Raedt

Publisher: Springer Science & Business Media

ISBN: 364213839X

Category: Mathematics

Page: 257

View: 306

This book constitutes the proceedings of the 19th International Conference on Inductive Logic Programming, held in Leuven, Belgium, in July 2009.

Advances in Inductive Logic Programming

This book is relevant to students, researchers and practitioners of artificial intelligence and computer science, especially those concerned with machine learning, data mining and computational logic.

Author: Luc de Raedt

Publisher: Ios PressInc

ISBN: 9789051992427

Category: Computers

Page: 323

View: 245

Inductive Logic Programming is a research area situated in machine learning and logic programming, two subfields of artificial intelligence. The goal of inductive logic programming is to develop theories, techniques and tools for inducing hypotheses from observations using the representations from computational logic. Inductive Logic Programming has a high potential for applications in data mining, automated scientific discovery, knowledge discovery in databases, as well as automatic programming. This book provides a detailed state-of-the-art overview of Inductive Logic Programming as well as a collection of recent technical contributions to Inductive Logic Programming. The state-of-the-art overview is based on - among others - the successful ESPRIT basic research project no. 6020 on Inductive Logic Programming, funded by the European Commission from 1992 till 1995. It highlights some of the most important recent results within Inductive Logic Programming and can be used as a thorough introduction to the field. This book is relevant to students, researchers and practitioners of artificial intelligence and computer science, especially those concerned with machine learning, data mining and computational logic.

Inductive Logic Programming

This book constitutes the refereed proceedings of the 18th International Conference on Inductive Logic Programming, ILP 2008, held in Prague, Czech Republic, in September 2008.

Author: Filip Železný

Publisher: Springer

ISBN: 3540859284

Category: Computers

Page: 358

View: 181

The 18th International Conference on Inductive Logic Programming was held in Prague, September 10–12, 2008. ILP returned to Prague after 11 years, and it is tempting to look at how the topics of interest have evolved during that time. The ILP community clearly continues to cherish its beloved ?rst-order logic representation framework. This is legitimate, as the work presented at ILP 2008 demonstrated that there is still room for both extending established ILP approaches (such as inverse entailment) and exploring novel logic induction frameworks (such as brave induction). Besides the topics lending ILP research its unique focus, we were glad to see in this year’s proceedings a good n- ber of papers contributing to areas such as statistical relational learning, graph mining, or the semantic web. To help open ILP to more mainstream research areas, the conference featured three excellent invited talks from the domains of the semantic web (Frank van Harmelen), bioinformatics (Mark Craven) and cognitive sciences (Josh Tenenbaum). We deliberately looked for speakers who are not directly involved in ILP research. We further invited a tutorial on stat- tical relational learning (Kristian Kersting) to meet the strong demand to have the topic presented from the ILP perspective. Lastly, Stefano Bertolo from the European Commission was invited to give a talk on the ideal niches for ILP in the current EU-supported research on intelligent content and semantics.

Inductive Logic Programming

This book constitutes the refereed proceedings of the 13th International Conference on Inductive Logic Programming, ILP 2003, held in Szeged, Hungary in September/October 2003.

Author: Tamas Horváth

Publisher: Springer Science & Business Media

ISBN: 9783540201441

Category: Computers

Page: 400

View: 586

This book constitutes the refereed proceedings of the 13th International Conference on Inductive Logic Programming, ILP 2003, held in Szeged, Hungary in September/October 2003. The 23 revised full papers presented were carefully reviewed and selected from 53 submissions. Among the topics addressed are multirelational data mining, complexity issues, theory revision, clustering, mathematical discovery, relational reinforcement learning, multirelational learning, inductive inference, description logics, grammar systems, and inductive learning.

Inductive Logic Programming

In this book they provide an extended, up-to-date survey of ILP, emphasizing methods and systems suitable for software engineering applications, including inductive program development, testing, and maintenance.

Author: Francesco Bergadano

Publisher: MIT Press

ISBN: 9780262023931

Category: Computers

Page: 240

View: 886

Although Inductive Logic Programming (ILP) is generally thought of as a research area at the intersection of machine learning and computational logic, Bergadano and Gunetti propose that most of the research in ILP has in fact come from machine learning, particularly in the evolution of inductive reasoning from pattern recognition, through initial approaches to symbolic machine learning, to recent techniques for learning relational concepts. In this book they provide an extended, up-to-date survey of ILP, emphasizing methods and systems suitable for software engineering applications, including inductive program development, testing, and maintenance. Inductive Logic Programming includes a definition of the basic ILP problem and its variations (incremental, with queries, for multiple predicates and predicate invention capabilities), a description of bottom-up operators and techniques (such as least general generalization, inverse resolution, and inverse implication), an analysis of top-down methods (mainly MIS and FOIL-like systems), and a survey of methods and languages for specifying inductive bias. Logic Programming series

Latest Advances in Inductive Logic Programming

This book represents a selection of papers presented at the Inductive Logic Programming (ILP) workshop held at Cumberland Lodge, Great Windsor Park. The collection marks two decades since the first ILP workshop in 1991.

Author: Stephen H Muggleton

Publisher: World Scientific

ISBN: 1783265108

Category: Computers

Page: 264

View: 161

This book represents a selection of papers presented at the Inductive Logic Programming (ILP) workshop held at Cumberland Lodge, Great Windsor Park. The collection marks two decades since the first ILP workshop in 1991. During this period the area has developed into the main forum for work on logic-based machine learning. The chapters cover a wide variety of topics, ranging from theory and ILP implementations to state-of-the-art applications in real-world domains. The international contributors represent leaders in the field from prestigious institutions in Europe, North America and Asia. Graduate students and researchers in this field will find this book highly useful as it provides an up-to-date insight into the key sub-areas of implementation and theory of ILP. For academics and researchers in the field of artificial intelligence and natural sciences, the book demonstrates how ILP is being used in areas as diverse as the learning of game strategies, robotics, natural language understanding, query search, drug design and protein modelling. Contents:Applications:Can ILP Learn Complete and Correct Game Strategies? (Stephen H Muggleton and Changze Xu)Induction in Nonmonotonic Causal Theories for a Domestic Service Robot (Jianmin Ji and Xiaoping Chen)Using Ontologies in Semantic Data Mining with g-SEGS and Aleph (Anže Vavpetič and Nada Lavră)Improving Search Engine Query Expansion Techniques with ILP (José Carlos Almeida Santos and Manuel Fonseca de Sam Bento Ribeiro)ILP for Cosmetic Product Selection (Hiroyuki Nishiyama and Fumio Mizoguchi)Learning User Behaviours in Real Mobile Domains (Andreas Markitanis, Domenico Corapi, Alessandra Russo and Emil C Lupu)Discovering Ligands for TRP Ion Channels Using Formal Concept Analysis (Mahito Sugiyama, Kentaro Imajo, Keisuke Otaki and Akihiro Yamamoto)Predictive Learning in Two-Way Datasets (Beau Piccart, Hendrik Blockeel, Andy Georges and Lieven Eeckhout)Model of Double-Strand Break of DNA in Logic-Based Hypothesis Finding (Barthelemy Dworkin, Andrei Doncescu, Jean-Charles Faye and Katsumi Inoue)Probabilistic Logical Learning:The PITA System for Logical-Probabilistic Inference (Fabrizio Riguzzi and Terrance Swift)Learning a Generative Failure-Free PRISM Clause (Waleed Alsanie and James Cussens)Statistical Relational Learning of Object Affordances for Robotic Manipulation (Bogdan Moldovan, Martijn van Otterlo, Plinio Moreno, José Santos-Victor and Luc De Raedt)Learning from Linked Data by Markov Logic (Man Zhu and Zhiqiang Gao)Satisfiability Machines (Filip Železný)Implementations:Customisable Multi-Processor Acceleration of Inductive Logic Programming (Andreas K Fidjeland, Wayne Luk and Stephen H Muggleton)Multivalue Learning in ILP (Orlando Muoz Texzocotetla and Ren Mac Kinney Romero)Learning Dependent-Concepts in ILP: Application to Model-Driven Data Warehouses (Moez Essaidi, Aomar Osmani and Céline Rouveirol)Graph Contraction Pattern Matching for Graphs of Bounded Treewidth (Takashi Yamada and Takayoshi Shoudai)mLynx: Relational Mutual Information (Nicola Di Mauro, Teresa M A Basile, Stefano Ferilli and Floriana Esposito)Theory:Machine Learning Coalgebraic Proofs (Ekaterina Komendantskaya)Can ILP Deal with Incomplete and Vague Structured Knowledge? (Francesca A Lisi and Umberto Straccia)Logical Learning:Towards Efficient Higher-Order Logic Learning in a First-Order Datalog Framework (Niels Pahlavi and Stephen H Muggleton)Automatic Invention of Functional Abstractions (Robert J Henderson and Stephen H Muggleton)Constraints:Using Machine-Generated Soft Constraints for Roster Problems (Yoshihisa Shiina and Hayato Ohwada)Spatial and Temporal:Relational Learning for Football-Related Predictions (Jan Van Haaren and Guy Van den Broeck) Readership: Graduate students and researchers in the field of ILP, and academics and researchers in the fields of artificial intelligence and natural sciences. Key Features:Covers major areas of research in ILPProvides an up-to-date insight into the key sub-areas of implementation and theory of ILPThe papers in this volume do not appear in conference proceedings elsewhere in the literatureKeywords:Machine Learning;Logic Programs;Inductive Inference;Structure Learning;Relational Learning;Statistical Relational Learning

Foundations of Inductive Logic Programming

The state of the art of the bioengineering aspects of the morphology of microorganisms and their relationship to process performance are described in this volume.

Author: Shan-Hwei Nienhuys-Cheng

Publisher: Springer Science & Business Media

ISBN: 9783540629276

Category: Computers

Page: 404

View: 146

The state of the art of the bioengineering aspects of the morphology of microorganisms and their relationship to process performance are described in this volume. Materials and methods of the digital image analysis and mathematical modeling of hyphal elongation, branching and pellet formation as well as their application to various fungi and actinomycetes during the production of antibiotics and enzymes are presented.

Inductive Logic Programming

Shan-HweiNienhuys-Cheng(UniversityofRotterdam) DavidPage(UniversityofLouisville) BernhardPfahringer(AustrianResearchInstituteforAI) CelineRouveirol(UniversityofParis) ClaudeSammut(UniversityofNewSouthWales) MicheleSebag(EcolePolytechnique) ...

Author: Saso Dzeroski

Publisher: Springer Science & Business Media

ISBN: 3540661093

Category: Computers

Page: 302

View: 237

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