01115nam0-2200337---450-99000994626040332120150318100011.0000994626FED01000994626(Aleph)000994626FED0100099462620150318d1957----km-y0itay50------bafreFRa-------001yyHistoire du peuple juif(des origines à la résurrection de l'Etat d'Israël)Cecil Rothtraduit de l'anglais par Ruth Schatzman et Anne-Marie Gentilyintroduction de M. Joseph Ariel2. èd. nouvelle édition revue et augmentéeParisÉditions de la Terre Retrouvée1957EbreiStoriaOrigini -1962956.94Roth,Cecil<1899-1970>137469Ariel,Joseph M.Gentily,Anne-MarieSchatzman,RuthITUNINARICAUNIMARCBK990009946260403321956.94 ROT 1Bibl. 30857FLFBCFLFBCHistoire du peuple juif820662UNINA00683oam 2200193z- 450 99638466570331620200818213413.0(CKB)4940000000067023(EEBO)2240905016(EXLCZ)99494000000006702320191209c1692uuuu -u- -engA copy of Admiral Russel's letter to the Earl of Nottingham. Published by authorityPrinted at London, and re-printed at EdinburghScotlandOrford Edward Russell, Earl of1020779BOOK996384665703316A copy of Admiral Russel's letter to the Earl of Nottingham. Published by authority2415750UNISA06468nam 22007935 450 991029899120332120230810211252.03-642-41251-310.1007/978-3-642-41251-6(CKB)3710000000269735(EBL)1968216(SSID)ssj0001372381(PQKBManifestationID)11831418(PQKBTitleCode)TC0001372381(PQKBWorkID)11304215(PQKB)11165435(MiAaPQ)EBC1968216(DE-He213)978-3-642-41251-6(PPN)182100049(EXLCZ)99371000000026973520141030d2014 u| 0engur|n|---|||||txtccrUncertainty Modeling for Data Mining A Label Semantics Approach /by Zengchang Qin, Yongchuan Tang1st ed. 2014.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2014.1 online resource (303 p.)Advanced Topics in Science and Technology in China,1995-6827Description based upon print version of record.3-642-41250-5 Includes bibliographical references.Cover; Title Page; Copyright Page; Dedication Page; Preface; Acknowledgements; Table of Contents; Acronyms; Notations; 1 Introduction; 1.1 Types of Uncertainty; 1.2 Uncertainty Modeling and Data Mining; 1.3 RelatedWorks; References; 2 Induction and Learning; 2.1 Introduction; 2.2 Machine Learning; 2.2.1 Searching in Hypothesis Space; 2.2.2 Supervised Learning; 2.2.3 Unsupervised Learning; 2.2.4 Instance-Based Learning; 2.3 Data Mining and Algorithms; 2.3.1 Why Do We Need Data Mining?; 2.3.2 How Do We do Data Mining?; 2.3.3 Artificial Neural Networks; 2.3.4 Support Vector Machines2.4 Measurement of Classifiers2.4.1 ROC Analysis for Classification; 2.4.2 Area Under the ROC Curve; 2.5 Summary; References; 3 Label Semantics Theory; 3.1 Uncertainty Modeling with Labels; 3.1.1 Fuzzy Logic; 3.1.2 Computing with Words; 3.1.3 Mass Assignment Theory; 3.2 Label Semantics; 3.2.1 Epistemic View of Label Semantics; 3.2.2 Random Set Framework; 3.2.3 Appropriateness Degrees; 3.2.4 Assumptions for Data Analysis; 3.2.5 Linguistic Translation; 3.3 Fuzzy Discretization; 3.3.1 Percentile-Based Discretization; 3.3.2 Entropy-Based Discretization; 3.4 Reasoning with Fuzzy Labels3.4.1 Conditional Distribution Given Mass Assignments3.4.2 Logical Expressions of Fuzzy Labels; 3.4.3 Linguistic Interpretation of Appropriate Labels; 3.4.4 Evidence Theory and Mass Assignment; 3.5 Label Relations; 3.6 Summary; References; 4 Linguistic Decision Trees for Classification; 4.1 Introduction; 4.2 Tree Induction; 4.2.1 Entropy; 4.2.2 Soft Decision Trees; 4.3 Linguistic Decision for Classification; 4.3.1 Branch Probability; 4.3.2 Classification by LDT; 4.3.3 Linguistic ID3 Algorithm; 4.4 Experimental Studies; 4.4.1 Influence of the Threshold; 4.4.2 Overlapping Between Fuzzy Labels4.5 Comparison Studies4.6 Merging of Branches; 4.6.1 Forward Merging Algorithm; 4.6.2 Dual-Branch LDTs; 4.6.3 Experimental Studies for Forward Merging; 4.6.4 ROC Analysis for Forward Merging; 4.7 Linguistic Reasoning; 4.7.1 Linguistic Interpretation of an LDT; 4.7.2 Linguistic Constraints; 4.7.3 Classification of Fuzzy Data; 4.8 Summary; References; 5 Linguistic Decision Trees for Prediction; 5.1 Prediction Trees; 5.2 Linguistic Prediction Trees; 5.2.1 Branch Evaluation; 5.2.2 Defuzzification; 5.2.3 Linguistic ID3 Algorithm for Prediction; 5.2.4 Forward Branch Merging for Prediction5.3 Experimental Studies5.3.1 3D Surface Regression; 5.3.2 Abalone and Boston Housing Problem; 5.3.3 Prediction of Sunspots; 5.3.4 Flood Forecasting; 5.4 Query Evaluation; 5.4.1 Single Queries; 5.4.2 Compound Queries; 5.5 ROC Analysis for Prediction; 5.5.1 Predictors and Probabilistic Classifiers; 5.5.2 AUC Value for Prediction; 5.6 Summary; References; 6 Bayesian Methods Based on Label Semantics; 6.1 Introduction; 6.2 Naive Bayes; 6.2.1 Bayes Theorem; 6.2.2 Fuzzy Naive Bayes; 6.3 Fuzzy Semi-Naive Bayes; 6.4 Online Fuzzy Bayesian Prediction; 6.4.1 Bayesian Methods; 6.4.2 Online Learning6.5 Bayesian Estimation TreesMachine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning.   Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China.Advanced Topics in Science and Technology in China,1995-6827Data miningArtificial intelligenceComputer networksComputer scienceMathematicsData Mining and Knowledge DiscoveryArtificial IntelligenceComputer Communication NetworksMathematical Applications in Computer ScienceData mining.Artificial intelligence.Computer networks.Computer scienceMathematics.Data Mining and Knowledge Discovery.Artificial Intelligence.Computer Communication Networks.Mathematical Applications in Computer Science.004004.0151005.7006.3006.312Qin Zengchangauthttp://id.loc.gov/vocabulary/relators/aut875231Tang Yongchuanauthttp://id.loc.gov/vocabulary/relators/autBOOK9910298991203321Uncertainty Modeling for Data Mining1954001UNINA