LEADER 05558nam 2200697 a 450 001 9910455562503321 005 20211202093958.0 010 $a1-282-75785-7 010 $a9786612757853 010 $a981-4271-07-1 035 $a(CKB)2490000000001739 035 $a(EBL)1679487 035 $a(OCoLC)859886714 035 $a(SSID)ssj0000424957 035 $a(PQKBManifestationID)11306088 035 $a(PQKBTitleCode)TC0000424957 035 $a(PQKBWorkID)10476709 035 $a(PQKB)10330674 035 $a(MiAaPQ)EBC1679487 035 $a(WSP)00000652 035 $a(Au-PeEL)EBL1679487 035 $a(CaPaEBR)ebr10422182 035 $a(CaONFJC)MIL275785 035 $a(EXLCZ)992490000000001739 100 $a20100520d2010 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aPattern classification using ensemble methods$b[electronic resource] /$fLior Rokach 210 $aSingapore ;$aHackensack, NJ $cWorld Scientific$dc2010 215 $a1 online resource (242 p.) 225 1 $aSeries in machine perception and artificial intelligence ;$vv. 75 300 $aDescription based upon print version of record. 311 $a981-4271-06-3 320 $aIncludes bibliographical references (p. 185-222) and index. 327 $aContents; Preface; 1. Introduction to Pattern Classification; 1.1 Pattern Classification; 1.2 Induction Algorithms; 1.3 Rule Induction; 1.4 Decision Trees; 1.5 Bayesian Methods; 1.5.1 Overview.; 1.5.2 Na?ve Bayes; 1.5.2.1 The Basic Na?ve Bayes Classifier; 1.5.2.2 Na?ve Bayes Induction for Numeric Attributes; 1.5.2.3 Correction to the Probability Estimation; 1.5.2.4 Laplace Correction; 1.5.2.5 No Match; 1.5.3 Other Bayesian Methods; 1.6 Other Induction Methods; 1.6.1 Neural Networks; 1.6.2 Genetic Algorithms; 1.6.3 Instance-based Learning; 1.6.4 Support Vector Machines 327 $a2. Introduction to Ensemble Learning 2.1 Back to the Roots; 2.2 The Wisdom of Crowds; 2.3 The Bagging Algorithm; 2.4 The Boosting Algorithm; 2.5 The Ada Boost Algorithm; 2.6 No Free Lunch Theorem and Ensemble Learning; 2.7 Bias-Variance Decomposition and Ensemble Learning; 2.8 Occam's Razor and Ensemble Learning; 2.9 Classifier Dependency; 2.9.1 Dependent Methods; 2.9.1.1 Model-guided Instance Selection; 2.9.1.2 Basic Boosting Algorithms; 2.9.1.3 Advanced Boosting Algorithms; 2.9.1.4 Incremental Batch Learning; 2.9.2 Independent Methods; 2.9.2.1 Bagging; 2.9.2.2 Wagging 327 $a2.9.2.3 Random Forest and Random Subspace Projection 2.9.2.4 Non-Linear Boosting Projection (NLBP); 2.9.2.5 Cross-validated Committees; 2.9.2.6 Robust Boosting; 2.10 Ensemble Methods for Advanced Classification Tasks; 2.10.1 Cost-Sensitive Classification; 2.10.2 Ensemble for Learning Concept Drift; 2.10.3 Reject Driven Classification; 3. Ensemble Classification; 3.1 Fusions Methods; 3.1.1 Weighting Methods; 3.1.2 Majority Voting; 3.1.3 Performance Weighting; 3.1.4 Distribution Summation; 3.1.5 Bayesian Combination; 3.1.6 Dempster-Shafer; 3.1.7 Vogging; 3.1.8 Na?ve Bayes 327 $a3.1.9 Entropy Weighting 3.1.10 Density-based Weighting; 3.1.11 DEA Weighting Method; 3.1.12 Logarithmic Opinion Pool; 3.1.13 Order Statistics; 3.2 Selecting Classification; 3.2.1 Partitioning the Instance Space; 3.2.1.1 The K-Means Algorithm as a Decomposition Tool; 3.2.1.2 Determining the Number of Subsets; 3.2.1.3 The Basic K-Classifier Algorithm; 3.2.1.4 The Heterogeneity Detecting K-Classifier (HDK-Classifier); 3.2.1.5 Running-Time Complexity; 3.3 Mixture of Experts and Meta Learning; 3.3.1 Stacking; 3.3.2 Arbiter Trees; 3.3.3 Combiner Trees; 3.3.4 Grading; 3.3.5 Gating Network 327 $a4. Ensemble Diversity 4.1 Overview; 4.2 Manipulating the Inducer; 4.2.1 Manipulation of the Inducer's Parameters; 4.2.2 Starting Point in Hypothesis Space; 4.2.3 Hypothesis Space Traversal; 4.3 Manipulating the Training Samples; 4.3.1 Resampling; 4.3.2 Creation; 4.3.3 Partitioning; 4.4 Manipulating the Target Attribute Representation; 4.4.1 Label Switching; 4.5 Partitioning the Search Space; 4.5.1 Divide and Conquer; 4.5.2 Feature Subset-based Ensemble Methods; 4.5.2.1 Random-based Strategy; 4.5.2.2 Reduct-based Strategy; 4.5.2.3 Collective-Performance-based Strategy 327 $a4.5.2.4 Feature Set Partitioning 330 $aResearchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions 410 0$aSeries in machine perception and artificial intelligence ;$vv. 75. 606 $aPattern recognition systems 606 $aAlgorithms 606 $aMachine learning 608 $aElectronic books. 615 0$aPattern recognition systems. 615 0$aAlgorithms. 615 0$aMachine learning. 676 $a621.389/28 700 $aRokach$b Lior$0620362 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910455562503321 996 $aPattern classification using ensemble methods$92100521 997 $aUNINA LEADER 05552nam 2200721 450 001 9910790533003321 005 20200520144314.0 010 $a1-118-73402-5 010 $a1-118-73405-X 035 $a(CKB)2550000001127211 035 $a(EBL)1426518 035 $a(SSID)ssj0001001052 035 $a(PQKBManifestationID)11541189 035 $a(PQKBTitleCode)TC0001001052 035 $a(PQKBWorkID)10961157 035 $a(PQKB)11494832 035 $a(DLC) 2013027943 035 $a(Au-PeEL)EBL1426518 035 $a(CaPaEBR)ebr10774378 035 $a(CaONFJC)MIL527875 035 $a(OCoLC)852745798 035 $a(CaSebORM)9781118734025 035 $a(MiAaPQ)EBC1426518 035 $a(EXLCZ)992550000001127211 100 $a20130705h20132013 uy| 0 101 0 $aeng 135 $aurunu||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBig data marketing $eengage your customers more effectively and drive value /$fLisa Arthur 205 $a1st edition 210 1$aHoboken, New Jersey :$cJohn Wiley & Sons, Inc.,$d[2013] 210 4$dİ2013 215 $a1 online resource (210 p.) 300 $aDescription based upon print version of record. 311 $a1-118-73389-4 311 $a1-299-96624-1 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Copyright; Contents; Foreword; Acknowledgments; Introduction; Part I The Problem: How Did We Get Here?; Chapter 1 Moving Out of the Dark Ages; The Threat of Digital Disruption; The Enlightened Age of Data; Chapter 2 Why Is Marketing Antiquated?; Tactical (versus Strategic) Marketing; Manual Marketing Management; Silos of Data and Demand for Real-time Engagement; Communicating the Value of Marketing; Lack of Talent and Training; Fragmented and Often Missing Data; Chapter 3 The Data Hairball; What Is the Data Hairball?; The Data Hairball and the Customer Experience 327 $aBlending Art and Science Integrated Marketing, Really; Data Privacy and Security; Part II Get Ready for Big Data Marketing; Chapter 4 Definitions for the Real World of Big Data Marketing; Big Data Terminology; Big Data Marketing; Integrated Marketing Management (IMM); Marketing Operations Management; Customer Interaction Management; Digital Messaging; Digital Marketing; Chapter 5 Meet the Modern Marketing Department (Michelangelo Meets Einstein); The CMO as a Change Agent; The Data Scientist; The CMO and CIO Dynamic; Part III The Five Steps to Data-Driven Marketing and Big Data Insights 327 $aChapter 6 Step One: Get Smart, Get Strategic Vision Leads to Strategy; Customer Interaction Strategy; Analytics Strategy; Data Strategy; Organizational Strategy; Technology Strategy; Chapter 7 Step Two: Tear Down the Silos; Tearing Down Silos Internal to Marketing; Tearing Down Silos between Marketing and Other Lines of Business; Developing a Strategic Framework for Synergy; New Best Friends: The CMO and the CIO; Chapter 8 Step Three: Untangle the Data Hairball; Start with Talent; Silos Can Threaten Big Data Strategy; Data Strategy; Discovering Big Data 327 $aBig Data Insights Combat Churn for US Telecommunications Provider Chapter 9 Step Four: Make Metrics Your Mantra; Use Metrics to Measure Outcomes; Lessons Learned from Cost per Lead; Part I: The ROI versus ROMI Debate; Part II: The ROI versus ROMMI Debate; Metrics Are the Cornerstone of Accountability; Metrics Improve Buy-In and Alignment; Reasons for Misalignment; Chapter 10 Step Five: Process Is the New Black; Process Is One of Marketing's New Four P's; Integrated Marketing Processes Accelerate Results; Concept to Campaign to Cash; Process Innovation at a Global IT Services Company 327 $aAgile Marketing Part IV Realizing the Value of Big Data Marketing; Chapter 11 Drive Value through Relevant Marketing; Internal Value through Integrating Marketing; External Value through Integrating Marketing; Chapter 12 The Bright, Enlightened World of Customer Experience; The People Marketing Challenge; The People Marketing Opportunity; The Mobile Marketing Challenge; The Mobile Marketing Opportunity; The Information Management Marketing Challenge; The Information Marketing Opportunity; The Big Data Marketing Challenge; The Big Data Marketing Opportunity; Notes; Resources; About the Author 327 $aIndex 330 $aLeverage big data insights to improve customer experiences and insure business success Many of today's businesses find themselves caught in a snarl of internal data, paralyzed by internal silos, and executing antiquated marketing approaches. As a result, consumers are losing patience, shareholders are clamoring for growth and differentiation, and marketers are left struggling to untangle the massive mess. Big Data Marketing provides a strategic road map for executives who want to clear the chaos and start driving competitive advantage and top line growth. Using real-world example 606 $aMarketing$xManagement 606 $aMarketing$xData processing 606 $aMarketing research$xStatistical methods 606 $aInternet marketing 615 0$aMarketing$xManagement. 615 0$aMarketing$xData processing. 615 0$aMarketing research$xStatistical methods. 615 0$aInternet marketing. 676 $a658.8/3 700 $aArthur$b Lisa$01569666 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910790533003321 996 $aBig data marketing$93842700 997 $aUNINA