LEADER 01616oam 2200421M 450 001 9911074595303321 005 20200213070613.7 035 $a(CKB)46360166600041 035 $a(DGPO)001197596 035 $a(OCoLC)1065975324 035 $a(EXLCZ)9946360166600041 100 $a20071213d1924 ua 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSalaries of Metropolitan Police, District of Columbia. December 6, 1924. -- Committed to the Committee of the Whole House on the State of the Union and ordered to be printed 210 1$aWashington, DC :$c[publisher not identified],$d1924. 215 $a1 online resource (1 page) 225 1 $aHouse report / 68th Congress, 2nd session. House ;$vno. 1035 225 1 $a[United States congressional serial set] ;$v[serial set no. 8390] 300 $aBatch processed record: Metadata reviewed, not verified. Some fields updated by batch processes. 300 $aFDLP item number not assigned. 606 $aWages 606 $aPolice 608 $aLegislative materials.$2lcgft 615 0$aWages. 615 0$aPolice. 701 $aZihlman$b Frederick Nicholas$f1879-1935.$pRepublican (MD)$01386186 801 0$bWYU 801 1$bWYU 801 2$bOCLCO 801 2$bOCLCQ 906 $aBOOK 912 $a9911074595303321 996 $aSalaries of Metropolitan Police, District of Columbia. December 6, 1924. -- Committed to the Committee of the Whole House on the State of the Union and ordered to be printed$94642411 997 $aUNINA LEADER 02750nam 2200589 a 450 001 9911141349603321 005 20250512143334.0 010 $a0-8262-6531-6 035 $a(CKB)1000000000467108 035 $a(SSID)ssj0000260827 035 $a(PQKBManifestationID)12047907 035 $a(PQKBTitleCode)TC0000260827 035 $a(PQKBWorkID)10225030 035 $a(PQKB)10270017 035 $a(MiAaPQ)EBC3570908 035 $a(EXLCZ)991000000000467108 100 $a20051128d2006 ub 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAwakening to equality $ea young white pastor at the dawn of civil rights /$fKarl E. Lutze 205 $a1st ed. 210 $aColumbia $cUniversity of Missouri Press$dc2006 215 $a1 online resource (175 pages) 300 $aIncludes index. 311 08$a0-8262-1632-3 327 $aIntro -- Awakening to Euality -- Contents -- Acknowledgments -- Introduction -- Chapter One The Door Opens -- Chapter Two Gender and Race - A Partner for the Experience -- Chapter Three Benign Whites -- Chapter Four The "Minority " People -- Chapter Five ...with Heels Dug In -- Chapter Six The Transition -- Chapter Seven The Supreme Court Speaks -- Chapter Eight Identifying New Allies -- Chapter Nine Catalyst for Change -- Chapter Ten The City Becomes Involved -- Chapter Eleven Population Spillover and Neighborhood Change -- Chapter Twelve Oklahoma,My Teacher -- Epilogue. 330 $a"In 1945, Karl Lutze was a young white pastor assigned to an African American church in Muskogee, Oklahoma. His experiences ministering to Black congregations there and, later, in Tulsa provide a unique perspective on the early civil rights movement in Oklahoma and within the Missouri Synod of the Lutheran Church"--Provided by publisher. 606 $aWhite people$zOklahoma$vBiography 606 $aLutheran Church$zOklahoma$xClergy$vBiography 606 $aAfrican Americans$xCivil rights$zOklahoma$xHistory$y20th century 606 $aCivil rights movements$zOklahoma$xHistory$y20th century 606 $aAfrican American churches$zOklahoma$xHistory$y20th century 607 $aMuskogee (Okla.)$xRace relations 607 $aTulsa (Okla.)$xRace relations 607 $aOklahoma$xRace relations 615 0$aWhite people 615 0$aLutheran Church$xClergy 615 0$aAfrican Americans$xCivil rights$xHistory 615 0$aCivil rights movements$xHistory 615 0$aAfrican American churches$xHistory 676 $a284.1092 676 $aB 700 $aLutze$b Karl E 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911141349603321 997 $aUNINA LEADER 05635nam 2200745 a 450 001 9911150319003321 005 20251117074605.0 010 $a9786612757853 010 $a9781282757851 010 $a1282757857 010 $a9789814271073 010 $a9814271071 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(Perlego)849088 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 /$fLior Rokach 205 $a1st ed. 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 $aDescription based upon print version of record. 311 08$a9789814271066 311 08$a9814271063 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 $aMachine learning 615 0$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 $a9911150319003321 996 $aPattern classification using ensemble methods$94865911 997 $aUNINA