Machine Learning of Inductive Bias, Paul E. Utgoff

Автор: Christopher M. Bishop
Название: Pattern Recognition and Machine Learning
ISBN: 0387310738 ISBN-13(EAN): 9780387310732
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Описание: Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Автор: Bert Lenaerts; Robert Puers
Название: Omnidirectional Inductive Powering for Biomedical Implants
ISBN: 9048180627 ISBN-13(EAN): 9789048180622
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Описание: This handbook on inductive link design investigates the feasibility of inductive powering for capsule endoscopy and freely moving systems in general. It is the only existing analysis on 3D inductive powering systems.

Автор: Luc De Raedt; Paolo Frasconi; Kristian Kersting; S
Название: Probabilistic Inductive Logic Programming
ISBN: 3540786511 ISBN-13(EAN): 9783540786511
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Описание: 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).

Автор: Hendrik Blockeel; Jan Ramon; Jude Shavlik; Prasad
Название: Inductive Logic Programming
ISBN: 3540784683 ISBN-13(EAN): 9783540784685
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Описание: 17th International Conference ILP 2007 Corvallis OR USA June 19-21 2007 Revised Selected Papers.

Автор: Koenraad van Schuylenbergh; Robert Puers
Название: Inductive Powering
ISBN: 9048184991 ISBN-13(EAN): 9789048184996
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Описание: Inductive powering is used to wirelessly power devices over relatively short distances. This handbook lists all design equations and topology alternatives to successfully build an inductive power and data link for your specific application.

Автор: Joakim Nivre
Название: Inductive Dependency Parsing
ISBN: 9048172187 ISBN-13(EAN): 9789048172184
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Описание: This book describes the framework of inductive dependency parsing, a methodology for robust and efficient syntactic analysis of unrestricted natural language text.

Автор: Jean-Francois Boulicaut; Luc De Raedt; Heikki Mann
Название: Constraint-Based Mining and Inductive Databases
ISBN: 3540313311 ISBN-13(EAN): 9783540313311
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Автор: Stephen Muggleton; Ramon Otero; Alireza Tamaddoni-
Название: Inductive Logic Programming
ISBN: 3540738460 ISBN-13(EAN): 9783540738466
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Описание: Constitutes the thoroughly refereed post-proceedings of the 16th International Conference on Inductive Logic Programming, ILP 2006, held in Santiago de Compostela, Spain, in August 2006. This work addresses various topics in inductive logic programming, ranging from theoretical and methodological issues to advanced applications in various areas.

Автор: Saso Dzeroski; Jan Struyf
Название: Knowledge Discovery in Inductive Databases
ISBN: 3540755489 ISBN-13(EAN): 9783540755487
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Описание: Constitutes the refereed joint postproceedings of the 5th International Workshop on Knowledge Discovery in Inductive Databases, KDID 2006, held in Berlin, Germany, September 18th, 2006 in association with ECML/PKDD. This book presents 15 revised full papers together with 1 invited paper that were selected during two rounds of reviewing.

Автор: Paul E. Utgoff
Название: Machine Learning of Inductive Bias
ISBN: 0898382238 ISBN-13(EAN): 9780898382235
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Описание: This book is based on the author's Ph.D. dissertation 56]. The the- sis research was conducted while the author was a graduate student in the Department of Computer Science at Rutgers University. The book was pre- pared at the University of Massachusetts at Amherst where the author is currently an Assistant Professor in the Department of Computer and Infor- mation Science. Programs that learn concepts from examples are guided not only by the examples (and counterexamples) that they observe, but also by bias that determines which concept is to be considered as following best from the ob- servations. Selection of a concept represents an inductive leap because the concept then indicates the classification of instances that have not yet been observed by the learning program. Learning programs that make undesir- able inductive leaps do so due to undesirable bias. The research problem addressed here is to show how a learning program can learn a desirable inductive bias.

Автор: D. Paul Benjamin
Название: Change of Representation and Inductive Bias
ISBN: 0792390555 ISBN-13(EAN): 9780792390558
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Описание: Change of Representation and Inductive Bias One of the most important emerging concerns of machine learning researchers is the dependence of their learning programs on the underlying representations, especially on the languages used to describe hypotheses.

Автор: D. Paul Benjamin
Название: Change of Representation and Inductive Bias
ISBN: 1461288177 ISBN-13(EAN): 9781461288176
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