Machine Learning of Inductive Bias, Paul E. Utgoff

Автор: Christopher M. Bishop
Название: Pattern Recognition and Machine Learning
ISBN: 0387310738 ISBN-13(EAN): 9780387310732
Издательство: Springer
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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: C.S. Wallace
Название: Statistical and Inductive Inference by Minimum Message Length
ISBN: 1441920153 ISBN-13(EAN): 9781441920157
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Описание: Mythanksareduetothemanypeoplewhohaveassistedintheworkreported here and in the preparation of this book. The work is incomplete and this account of it rougher than it might be. Such virtues as it has owe much to others; the faults are all mine. MyworkleadingtothisbookbeganwhenDavidBoultonandIattempted to develop a method for intrinsic classi?cation. Given data on a sample from some population, we aimed to discover whether the population should be considered to be a mixture of di?erent types, classes or species of thing, and, if so, how many classes were present, what each class looked like, and which things in the sample belonged to which class. I saw the problem as one of Bayesian inference, but with prior probability densities replaced by discrete probabilities re?ecting the precision to which the data would allow parameters to be estimated. Boulton, however, proposed that a classi?cation of the sample was a way of brie?y encoding the data: once each class was described and each thing assigned to a class, the data for a thing would be partially implied by the characteristics of its class, and hence require little further description. After some weeks arguing our cases, we decided on the maths for each approach, and soon discovered they gave essentially the same results. Without Boulton s insight, we may never have made the connection between inference and brief encoding, which is the heart of this work."

Автор: Joakim Nivre
Название: Inductive Dependency Parsing
ISBN: 9048172187 ISBN-13(EAN): 9789048172184
Издательство: Springer
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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
Издательство: Springer
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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: 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
Издательство: Springer
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Описание: 17th International Conference ILP 2007 Corvallis OR USA June 19-21 2007 Revised Selected Papers.

Автор: Catarina Silva; Bernadete Ribeiro
Название: Inductive Inference for Large Scale Text Classification
ISBN: 3642261345 ISBN-13(EAN): 9783642261343
Издательство: Springer
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Цена: 113180.00 T
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Описание: This book explains and illustrates key methods in inductive inference in large scale text classification, especially kernel approaches. It covers a series of new techniques to enhance, scale and distribute text classification tasks.

Автор: Koenraad van Schuylenbergh; Robert Puers
Название: Inductive Powering
ISBN: 9048124115 ISBN-13(EAN): 9789048124114
Издательство: Springer
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Описание: Inductive powering has been a reliable and simple method for many years to wirelessly power devices over relatively short distances, from a few centimetres to a few feet. This work lists various design equations and topology alternatives to successfully build an inductive power and data link for your specific application.

Автор: Catarina Silva; Bernadete Ribeiro
Название: Inductive Inference for Large Scale Text Classification
ISBN: 3642045324 ISBN-13(EAN): 9783642045325
Издательство: Springer
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Цена: 139310.00 T
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Описание: This book explains and illustrates key methods in inductive inference in large scale text classification, especially kernel approaches. It covers a series of new techniques to enhance, scale and distribute text classification tasks.