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5 produkter
5 produkter
1 127 kr
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Modeling the Internet and the Web covers the most important aspects of modeling the Web using a modern mathematical and probabilistic treatment. It focuses on the information and application layers, as well as some of the emerging properties of the Internet. Provides a comprehensive introduction to the modeling of the Internet and the Web at the information level. Takes a modern approach based on mathematical, probabilistic, and graphical modeling. Provides an integrated presentation of theory, examples, exercises and applications. Covers key topics such as text analysis, link analysis, crawling techniques, human behaviour, and commerce on the Web.Interdisciplinary in nature, Modeling the Internet and the Web will be of interest to students and researchers from a variety of disciplines including computer science, machine learning, engineering, statistics, economics, business, and the social sciences."This book is fascinating!" - David Hand (Imperial College, UK)"This book provides an extremely useful introduction to the intellectually stimulating problems of data mining electronic business." - Andreas S. Weigend (Chief Scientist, Amazon.com)
Del 9851 - Lecture Notes in Computer Science
Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part I
Häftad, Engelska, 2016
1 099 kr
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The three volume set LNAI 9851, LNAI 9852, and LNAI 9853 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2016, held in Riva del Garda, Italy, in September 2016.
Del 9852 - Lecture Notes in Computer Science
Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II
Häftad, Engelska, 2016
1 099 kr
Skickas inom 10-15 vardagar
The three volume set LNAI 9851, LNAI 9852, and LNAI 9853 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2016, held in Riva del Garda, Italy, in September 2016.
553 kr
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One of the key open questions within arti?cial intelligence is how to combine probability and logic with learning. This question is getting an increased - tentioninseveraldisciplinessuchasknowledgerepresentation,reasoningabout uncertainty, data mining, and machine learning simulateously, resulting in the newlyemergingsub?eldknownasstatisticalrelationallearningandprobabil- ticinductivelogicprogramming.Amajordriving forceisthe explosivegrowth in the amount of heterogeneous data that is being collected in the business and scienti?c world. Example domains include bioinformatics, chemoinform- ics, transportation systems, communication networks, social network analysis, linkanalysis,robotics,amongothers.Thestructuresencounteredcanbeass- pleassequencesandtrees(suchasthosearisinginproteinsecondarystructure predictionandnaturallanguageparsing)orascomplexascitationgraphs,the WorldWideWeb,andrelationaldatabases. This book providesan introduction to this ?eld with an emphasison those methods based on logic programming principles. The book is also the main resultofthesuccessfulEuropeanISTFETprojectno.FP6-508861onAppli- tionofProbabilisticInductiveLogicProgramming(APRILII,2004-2007).This projectwascoordinatedbytheAlbertLudwigsUniversityofFreiburg(Germany, Luc De Raedt) and the partners were Imperial College London (UK, Stephen MuggletonandMichaelSternberg),theHelsinkiInstituteofInformationTe- nology(Finland,HeikkiMannila),theUniversit' adegliStudidiFlorence(Italy, PaoloFrasconi),andtheInstitutNationaldeRechercheenInformatiqueet- tomatiqueRocquencourt(France,FrancoisFages).Itwasconcernedwiththeory, implementationsandapplicationsofprobabilisticinductivelogicprogramming. Thisstructureisalsore?ectedinthebook. The book starts with an introductory chapter to "Probabilistic Inductive LogicProgramming"byDeRaedtandKersting.Inasecondpart,itprovidesa detailedoverviewofthemostimportantprobabilisticlogiclearningformalisms and systems. We are very pleased and proud that the scientists behind the key probabilistic inductive logic programming systems (also those developed outside the APRIL project) have kindly contributed a chapter providing an overviewoftheircontributions.Thisincludes:relationalsequencelearningte- niques (Kersting et al.), using kernels with logical representations (Frasconi andPasserini),MarkovLogic(Domingosetal.), the PRISMsystem (Satoand Kameya),CLP(BN)(SantosCostaetal.),BayesianLogicPrograms(Kersting andDeRaedt),andtheIndependentChoiceLogic(Poole).Thethirdpartthen provides a detailed account of some show-caseapplications of probabilistic - ductive logic programming, more speci?cally: in protein fold discovery (Chen et al.), haplotyping (Landwehr and Mielik. ainen) and systems biology (Fages andSoliman). The ?nal parttouchesupon sometheoreticalinvestigationsand VI Preface includes chaptersonbehavioralcomparisonof probabilisticlogicprogramming representations(MuggletonandChen)andamodel-theoreticexpressivityan- ysis(Jaeger).
Inductive Logic Programming
20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010, Revised Papers
Häftad, Engelska, 2011
553 kr
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This book constitutes the thoroughly refereed post-proceedings of the 20th International Conference on Inductive Logic Programming, ILP 2010, held in Florence, Italy in June 2010.The 11 revised full papers and 15 revised short papers presented together with abstracts of three invited talks were carefully reviewed and selected during two rounds of refereeing and revision. All current issues in inductive logic programming, i.e. in logic programming for machine learning are addressed, in particular statistical learning and other probabilistic approaches to machine learning are reflected.