LEADER 01169nam0 2200289 450 001 000037919 005 20140901113233.0 100 $a20140820d1968----km-y0itaa50------ba 101 0 $aeng$afre 102 $aCH 200 1 $aAesthetic distance in Chretien de Troyes : irony and comedy in Cliges and Perceval$fPeter Haidu 210 $aGenève$c<> Droz$d1968 215 $a272 p.$d24 cm 225 2 $aHistoire des idées et critique littéraire 410 0$12001$aHistoire des idées et critique littéraire 600 1$aChrétien,$bde Troyes 676 $a840.1$v(22. ed.)$9Letteratura francese. Primo periodo, fino al 1399 700 1$aHaidu,$bPeter$0184401 801 0$aIT$bUniversità della Basilicata - B.I.A.$gREICAT$2unimarc 912 $a000037919 996 $aAesthetic distance in Chretien de Troyes : irony and comedy in Cliges and Perceval$9100287 997 $aUNIBAS BAS $aLETTERE CAT $aEXT017$b01$c20140820$lBAS01$h1324 CAT $aEXT017$b01$c20140901$lBAS01$h1132 FMT Z30 -1$lBAS01$LBAS01$mBOOK$1BASA1$APolo Storico-Umanistico$2DSLF$BCollezione DiSLF$3DF/E966$6999$5F999$820140820$f04$FPrestabile Didattica LEADER 05422nam 2200673 450 001 9910141572403321 005 20221206103809.0 010 $a1-118-64633-9 010 $a1-118-64620-7 024 7 $a10.1002/9781118646106 035 $a(CKB)2670000000360077 035 $a(EBL)1204742 035 $a(SSID)ssj0000886212 035 $a(PQKBManifestationID)11487395 035 $a(PQKBTitleCode)TC0000886212 035 $a(PQKBWorkID)10816368 035 $a(PQKB)10497643 035 $a(MiAaPQ)EBC1204742 035 $a(DLC) 2013019555 035 $a(CaBNVSL)mat06542371 035 $a(IDAMS)0b00006481da1ac4 035 $a(IEEE)6542371 035 $a(OCoLC)843228806 035 $a(EXLCZ)992670000000360077 100 $a20151222d2013 uy 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aImbalanced learning $efoundations, algorithms, and applications /$fedited by Haibo He, Yunqian Ma 210 1$aPiscataway, NJ :$cIEEE Press ;$aHoboken, New Jersey :$cWiley,$d[2013] 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2013] 215 $a1 online resource (224 p.) 300 $aDescription based upon print version of record. 311 $a1-118-64610-X 311 $a1-118-07462-9 320 $aIncludes bibliographical references and index. 327 $aPreface ix -- Contributors xi -- 1 Introduction 1 -- Haibo He -- 1.1 Problem Formulation, 1 -- 1.2 State-of-the-Art Research, 3 -- 1.3 Looking Ahead: Challenges and Opportunities, 6 -- 1.4 Acknowledgments, 7 -- References, 8 -- 2 Foundations of Imbalanced Learning 13 -- Gary M. Weiss -- 2.1 Introduction, 14 -- 2.2 Background, 14 -- 2.3 Foundational Issues, 19 -- 2.4 Methods for Addressing Imbalanced Data, 26 -- 2.5 Mapping Foundational Issues to Solutions, 35 -- 2.6 Misconceptions About Sampling Methods, 36 -- 2.7 Recommendations and Guidelines, 38 -- References, 38 -- 3 Imbalanced Datasets: From Sampling to Classifiers 43 -- T. Ryan Hoens and Nitesh V. Chawla -- 3.1 Introduction, 43 -- 3.2 Sampling Methods, 44 -- 3.3 Skew-Insensitive Classifiers for Class Imbalance, 49 -- 3.4 Evaluation Metrics, 52 -- 3.5 Discussion, 56 -- References, 57 -- 4 Ensemble Methods for Class Imbalance Learning 61 -- Xu-Ying Liu and Zhi-Hua Zhou -- 4.1 Introduction, 61 -- 4.2 Ensemble Methods, 62 -- 4.3 Ensemble Methods for Class Imbalance Learning, 66 -- 4.4 Empirical Study, 73 -- 4.5 Concluding Remarks, 79 -- References, 80 -- 5 Class Imbalance Learning Methods for Support Vector Machines 83 -- Rukshan Batuwita and Vasile Palade -- 5.1 Introduction, 83 -- 5.2 Introduction to Support Vector Machines, 84 -- 5.3 SVMs and Class Imbalance, 86 -- 5.4 External Imbalance Learning Methods for SVMs: Data Preprocessing Methods, 87 -- 5.5 Internal Imbalance Learning Methods for SVMs: Algorithmic Methods, 88 -- 5.6 Summary, 96 -- References, 96 -- 6 Class Imbalance and Active Learning 101 -- Josh Attenberg and Sd eyda Ertekin -- 6.1 Introduction, 102 -- 6.2 Active Learning for Imbalanced Problems, 103 -- 6.3 Active Learning for Imbalanced Data Classification, 110 -- 6.4 Adaptive Resampling with Active Learning, 122 -- 6.5 Difficulties with Extreme Class Imbalance, 129 -- 6.6 Dealing with Disjunctive Classes, 130 -- 6.7 Starting Cold, 132 -- 6.8 Alternatives to Active Learning for Imbalanced Problems, 133. 327 $a6.9 Conclusion, 144 -- References, 145 -- 7 Nonstationary Stream Data Learning with Imbalanced Class Distribution 151 -- Sheng Chen and Haibo He -- 7.1 Introduction, 152 -- 7.2 Preliminaries, 154 -- 7.3 Algorithms, 157 -- 7.4 Simulation, 167 -- 7.5 Conclusion, 182 -- 7.6 Acknowledgments, 183 -- References, 184 -- 8 Assessment Metrics for Imbalanced Learning 187 -- Nathalie Japkowicz -- 8.1 Introduction, 187 -- 8.2 A Review of Evaluation Metric Families and their Applicability -- to the Class Imbalance Problem, 189 -- 8.3 Threshold Metrics: Multiple- Versus Single-Class Focus, 190 -- 8.4 Ranking Methods and Metrics: Taking Uncertainty into Consideration, 196 -- 8.5 Conclusion, 204 -- 8.6 Acknowledgments, 205 -- References, 205 -- Index 207. 330 $aSolving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, and defense, to name a few. The first comprehensive look at this new branch of machine learning, this volume offers a critical review of the problem of imbalanced learning, covering the state-of-the-art in techniques, principles, and real-world applications. 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