Handbook of Statistics: Machine Learning: Theory and Applications PDF
Preview Handbook of Statistics: Machine Learning: Theory and Applications
HANDBOOK OF STATISTICS VOLUME 31 Handbook of Statistics VOLUME 31 General Editor C.R. Rao C.R. RaoAIMSCS, Universityof HyderabadCampus, Hyderabad, India Amsterdam Boston Heidelberg London NewYork Oxford d d d d d Paris SanDiego SanFrancisco Singapore Sydney Tokyo d d d d d Volume 31 Machine Learning: Theory and Applications Edited by Venu Govindaraju Universityat Buffalo, Buffalo, USA C.R. Rao C.R. RaoAIMSCS, University of HyderabadCampus, Hyderabad,India Amsterdam Boston Heidelberg London NewYork Oxford d d d d d Paris SanDiego SanFrancisco Singapore Sydney Tokyo d d d d d North-HollandisanimprintofElsevier North-HollandisanimprintofElsevier TheBoulevard,LangfordLane,Kidlington,Oxford,OX51GB,UK Radarweg29,POBox211,1000AEAmsterdam,TheNetherlands Firstedition2013 Copyright(cid:2)2013ElsevierB.V.Allrightsreserved. Nopartofthispublicationmaybereproduced,storedinaretrievalsystemortransmitted inanyformorbyanymeanselectronic,mechanical,photocopying,recordingorotherwise withoutthepriorwrittenpermissionofthepublisher. Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting ObtainingpermissiontouseElseviermaterial. Notice Noresponsibilityisassumedbythepublisherforanyinjuryand/ordamagetopersonsor property as a matter of products liability, negligence or otherwise, or from any use or operationofanymethods,products,instructionsorideascontainedinthematerialherein. Becauseofrapidadvancesinthemedicalsciences,inparticular,independentverificationof diagnosesanddrugdosagesshouldbemade. BritishLibraryCataloguinginPublicationData AcataloguerecordforthisbookisavailablefromtheBritishLibrary. LibraryofCongressCataloging-in-PublicationData AcatalogrecordforthisbookisavailablefromtheLibraryofCongress. ISBN:978-0-444-53859-8 ISSN:0169-7161 ForinformationonallNorth-Hollandpublications visitourwebsiteatstore.elsevier.com PrintedandboundinGreatBritain 13141516 10 9 8 7 6 5 4 3 2 1 Table of Contents Volume 31 Handbook of Statistics Contributors: Vol. 31 xi Preface to Handbook Volume – 31 xv Introduction xvii Part I: Theoretical Aspects 1 Ch. 1. The Sequential Bootstrap 3 P.K. Pathak and C.R. Rao 1. Introduction 4 2. Asequentialbootstrapresamplingscheme 7 3. Bootstrappingempiricalmeasureswitharandomsamplesize 9 4. Convergenceratesforthesequentialbootstrap 12 5. Second-ordercorrectnessofthesequentialbootstrap 14 6. Concludingremarks 17 Acknowledgments 17 References 18 Ch. 2. The Cross-Entropy Method for Estimation 19 Dirk P. Kroese, Reuven Y. Rubinstein, and Peter W. Glynn 1. Introduction 19 2. Estimationsetting 20 3. Extensions 26 Acknowledgment 33 References 33 v vi TableofContents Ch. 3. The Cross-Entropy Method for Optimization 35 Zdravko I. Botev, Dirk P. Kroese, Reuven Y. Rubinstein, and Pierre L’Ecuyer 1. Introduction 35 2. Fromestimationtooptimization 36 3. Applicationstocombinatorialoptimization 40 4. Continuousoptimization 50 5. Summary 57 References 57 Ch. 4. Probability Collectives in Optimization 61 David H. Wolpert, Stefan R. Bieniawski, and Dev G. Rajnarayan 1. Introduction 61 2. Delayedsamplingtheory 64 3. Delayedsamplingexperiments 70 4. Immediatesamplingtheory 82 5. Immediatesamplingexperiments 85 6. Conclusion 97 References 98 Ch. 5. Bagging, Boosting, and Random Forests Using R 101 Hansen Bannerman-Thompson, M. Bhaskara Rao, and Subramanyam Kasala 1. Introduction 101 2. Datasetsandrationale 103 3. Bagging 107 4. Boosting 113 5. DoBaggingandBoostingreallywork? 115 6. Whatisaclassificationtree? 116 7. Classificationtreeversuslogisticregression 126 8. Randomforest 127 9. Randomforest,genetics,andcross-validation 134 10. Regressiontrees 139 11. BoostingusingtheRpackage,ada 144 12. Epilog 149 References 149 Ch. 6. Matching Score Fusion Methods 151 Sergey Tulyakov and Venu Govindaraju 1. Introduction 151 2. Matchingsystems 156 3. Selectedapproachestofusioninmatchingsystems 158 4. Operatingmodesofmatchingsystems 162 5. Complexitytypesofclassifiercombinationmethods 163 6. Modelingmatchingscoredependencies 165 7. Scorecombinationapplications 167 8. Conclusion 169 Appendices 169 TableofContents vii A. ProofofClaim4 169 B. ProofofClaim5 171 References 173 Part II: Object Recognition 177 Ch. 7. Statistical Methods on Special Manifolds for Image and Video Understanding 179 Pavan Turaga, Rama Chellappa, and Anuj Srivastava 1. Introduction 179 2. Somemotivatingexamples 180 3. Differentialgeometrictools 181 4. Commonmanifoldsarisinginimageanalysis 185 5. Applicationsinimageanalysis 187 6. Summaryanddiscussion 198 Acknowledgments 198 References 198 Ch. 8. Dictionary-Based Methods for Object Recognition 203 Vishal M. Patel and Rama Chellappa 1. Introduction 203 2. Sparserepresentation 204 3. Dictionarylearning 210 4. Concludingremarks 222 References 222 Ch. 9. Conditional Random Fields for Scene Labeling 227 Ifeoma Nwogu and Venu Govindaraju 1. Introduction 227 2. OverviewofCRF 229 3. Sceneparsing 236 4. MorerecentimplementationsofCRFscenelabelings 242 5. Conclusionandfuturedirections 245 References 245 Ch. 10.Shape-Based Image Classification and Retrieval 249 N. Mohanty, A. Lee-St. John, R. Manmatha, and T.M. Rath 1. Introduction 250 2. Priorwork 251 3. Classificationandretrievalmodels 252 4. Features 257 5. Classificationexperiments 261 6. Retrieval 262 viii TableofContents 7. Multipleclasslabels 265 8. Summaryandconclusions 266 References 266 Ch. 11.Visual Search: A Large-Scale Perspective 269 Robinson Piramuthu, Anurag Bhardwaj, Wei Di, and Neel Sundaresan 1. Introduction 269 2. Whenisbigdataimportant? 272 3. Informationextractionandrepresentation 273 4. Matchingimages 282 5. Practicalconsiderations:memoryfootprintandspeed 285 6. Benchmarkdatasets 289 7. Closingremarks 291 References 293 Part III: Biometric Systems 299 Ch. 12.VideoActivityRecognitionbyLuminanceDifferentialTrajectoryandAligned Projection Distance 301 Haomian Zheng, Zhu Li, Yun Fu, Aggelos K. Katsaggelos, and Jane You 1. Introduction 302 2. Relatedwork 303 3. Problemformulation 305 4. DLFTandLAPDsolutions 312 5. Experiments 314 6. Conclusion 323 References 324 Ch. 13.Soft Biometrics for Surveillance: An Overview 327 D.A. Reid, S. Samangooei, C. Chen, M.S. Nixon, and A. Ross 1. Introduction 327 2. Performancemetrics 329 3. Incorporatingsoftbiometricsinafusionframework 330 4. Humanidentificationusingsoftbiometrics 333 5. Predictinggenderfromfaceimages 344 6. Applications 346 7. Conclusion 349 References 350 Ch. 14.A User Behavior Monitoring and Profiling Scheme for Masquerade Detection 353 Ashish Garg, Shambhu Upadhyaya, and Kevin Kwiat 1. Introduction 354 2. Relatedwork 356 3. SupportVectorMachines(SVMs) 358 TableofContents ix 4. Datacollection,featureextraction,andfeaturevectorgeneration 361 5. Experimentaldesign 367 6. Discussionandconclusion 376 Acknowledgments 376 References 377 Ch. 15.Application of Bayesian Graphical Models to Iris Recognition 381 B.V.K. Vijaya Kumar, Vishnu Naresh Boddeti, Jonathon M. Smereka, Jason Thornton, and Marios Savvides 1. Introduction 381 2. Gaborwavelet-basedmatching 383 3. Correlationfilter-basedirismatching 386 4. Bayesiangraphicalmodelforirisrecognition 390 5. Summary 397 Acknowledgments 397 References 397 Part IV: Document Analysis 399 Ch. 16.Learning Algorithms for Document Layout Analysis 401 Simone Marinai 1. Introduction 401 2. Pixelclassification 405 3. Zoneclassification 406 4. Connectedcomponentclassification 409 5. Textregionsegmentation 411 6. Regionclassification 412 7. Functionallabeling 415 8. Conclusion 416 References 416 Ch. 17.Hidden Markov Models for Off-Line Cursive Handwriting Recognition 421 Andreas Fischer, Volkmar Frinken, and Horst Bunke 1. Introduction 421 2. Serializationofhandwritingimages 423 3. HMM-basedtextlinerecognition 426 4. Outlookandconclusions 438 Acknowledgment 439 References 439 Ch. 18.Machine Learning in Handwritten Arabic Text Recognition 443 Utkarsh Porwal, Zhixin Shi, and Srirangaraj Setlur 1. Introduction 443 2. Arabicscript—challengesforrecognition 444 3. Learningparadigms 447 4. Featuresfortextrecognition 455
The list of books you might like
