- Page 1 and 2: Combining Pattern Classifiers
- Page 3 and 4: Copyright # 2004 by John Wiley & So
- Page 5 and 6: vi CONTENTS 1.5.2 Normal Distributi
- Page 7 and 8: viii CONTENTS 5.1.2 Probabilities B
- Page 9 and 10: x CONTENTS 9.1.1 Equivalence of MIN
- Page 11 and 12: Preface Everyday life throws at us
- Page 13 and 14: PREFACE xv employing it for buildin
- Page 15: Notation and Acronyms CART LDC MCS
- Page 19 and 20: BASIC CONCEPTS: CLASS, FEATURE, AND
- Page 21 and 22: CLASSIFIER, DISCRIMINANT FUNCTIONS,
- Page 23 and 24: CLASSIFIER, DISCRIMINANT FUNCTIONS,
- Page 25 and 26: CLASSIFICATION ERROR AND CLASSIFICA
- Page 27 and 28: CLASSIFICATION ERROR AND CLASSIFICA
- Page 29 and 30: EXPERIMENTAL COMPARISON OF CLASSIFI
- Page 31 and 32: EXPERIMENTAL COMPARISON OF CLASSIFI
- Page 33 and 34: EXPERIMENTAL COMPARISON OF CLASSIFI
- Page 35 and 36: EXPERIMENTAL COMPARISON OF CLASSIFI
- Page 37 and 38: EXPERIMENTAL COMPARISON OF CLASSIFI
- Page 39 and 40: EXPERIMENTAL COMPARISON OF CLASSIFI
- Page 41 and 42: BAYES DECISION THEORY 25 4. Make su
- Page 43 and 44: BAYES DECISION THEORY 27 Fig. 1.8 N
- Page 45 and 46: BAYES DECISION THEORY 29 coming fro
- Page 47 and 48: BAYES DECISION THEORY 31 because th
- Page 49 and 50: BAYES DECISION THEORY 33 The error
- Page 51 and 52: TAXONOMY OF CLASSIFIER DESIGN METHO
- Page 53 and 54: Holmström et al. [32] consider ano
- Page 55 and 56: K-HOLD-OUT PAIRED t-TEST 39 Fig. 1.
- Page 57 and 58: 5 2cv PAIRED t-TEST 41 for i=1:K l
- Page 59 and 60: DATA GENERATION: LISSAJOUS FIGURE D
- Page 61 and 62: 46 BASE CLASSIFIERS where P(v i ) i
- Page 63 and 64: 48 BASE CLASSIFIERS For the common
- Page 65 and 66: 50 BASE CLASSIFIERS error was 42.8
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52 BASE CLASSIFIERS Fig. 2.2 Classi
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54 BASE CLASSIFIERS 2.2.2 Parzen Cl
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56 BASE CLASSIFIERS 2.3 THE k-NEARE
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58 BASE CLASSIFIERS Fig. 2.4 Illust
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60 BASE CLASSIFIERS set are called
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62 BASE CLASSIFIERS TABLE 2.2 Error
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64 BASE CLASSIFIERS justification.
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66 BASE CLASSIFIERS Fig. 2.7 Illust
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68 BASE CLASSIFIERS For a ¼ 0 and
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70 BASE CLASSIFIERS Fig. 2.8 (a) An
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72 BASE CLASSIFIERS 2.4.2.3 Misclas
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74 BASE CLASSIFIERS 2.4.3 Stopping
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76 BASE CLASSIFIERS TABLE 2.4 A Tab
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78 BASE CLASSIFIERS then calculate
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80 BASE CLASSIFIERS pay off. Method
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82 BASE CLASSIFIERS mation involved
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84 BASE CLASSIFIERS . The threshold
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86 BASE CLASSIFIERS Fig. 2.16 (a) U
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88 BASE CLASSIFIERS Fig. 2.18 Possi
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90 BASE CLASSIFIERS Eq. (2.90) or E
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92 BASE CLASSIFIERS of E (the updat
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94 BASE CLASSIFIERS For input-to-hi
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96 BASE CLASSIFIERS chi2=tree_chi2(
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98 BASE CLASSIFIERS ind=1; leaf=0;
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100 BASE CLASSIFIERS % outputs of t
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102 MULTIPLE CLASSIFIER SYSTEMS Fig
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104 MULTIPLE CLASSIFIER SYSTEMS Fig
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106 MULTIPLE CLASSIFIER SYSTEMS is
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108 MULTIPLE CLASSIFIER SYSTEMS . u
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110 MULTIPLE CLASSIFIER SYSTEMS Rus
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112 FUSION OF LABEL OUTPUTS general
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114 FUSION OF LABEL OUTPUTS TABLE 4
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116 FUSION OF LABEL OUTPUTS and U m
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118 FUSION OF LABEL OUTPUTS TABLE 4
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120 FUSION OF LABEL OUTPUTS To rela
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122 FUSION OF LABEL OUTPUTS where j
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124 FUSION OF LABEL OUTPUTS Assigni
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126 FUSION OF LABEL OUTPUTS 4.4 NAI
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128 FUSION OF LABEL OUTPUTS s 2 ¼
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130 FUSION OF LABEL OUTPUTS CI(v j
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132 FUSION OF LABEL OUTPUTS first-o
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134 FUSION OF LABEL OUTPUTS The mut
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136 FUSION OF LABEL OUTPUTS Constru
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138 FUSION OF LABEL OUTPUTS TABLE 4
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140 FUSION OF LABEL OUTPUTS To labe
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142 FUSION OF LABEL OUTPUTS Fig. 4.
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144 FUSION OF LABEL OUTPUTS Next we
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146 FUSION OF LABEL OUTPUTS 3. None
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148 FUSION OF LABEL OUTPUTS Similar
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5 Fusion of Continuous- Valued Outp
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HOW DO WE GET PROBABILITY OUTPUTS?
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HOW DO WE GET PROBABILITY OUTPUTS?
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CLASS-CONSCIOUS COMBINERS 157 As se
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CLASS-CONSCIOUS COMBINERS 159 Fig.
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CLASS-CONSCIOUS COMBINERS 161 Fig.
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CLASS-CONSCIOUS COMBINERS 163 The c
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The mean of the error of D 1 is 0:2
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CLASS-CONSCIOUS COMBINERS 167 befor
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CLASS-CONSCIOUS COMBINERS 169 The f
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CLASS-INDIFFERENT COMBINERS 171 par
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CLASS-INDIFFERENT COMBINERS 173 The
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CLASS-INDIFFERENT COMBINERS 175 the
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WHERE DO THE SIMPLE COMBINERS COME
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WHERE DO THE SIMPLE COMBINERS COME
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WHERE DO THE SIMPLE COMBINERS COME
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WHERE DO THE SIMPLE COMBINERS COME
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WHERE DO THE SIMPLE COMBINERS COME
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COMMENTS 187 and the weighted produ
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6 Classifier Selection 6.1 PRELIMIN
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WHY CLASSIFIER SELECTION WORKS 191
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ESTIMATING LOCAL COMPETENCE DYNAMIC
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ESTIMATING LOCAL COMPETENCE DYNAMIC
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PREESTIMATION OF THE COMPETENCE REG
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SELECTION OR FUSION? 199 TABLE 6.2
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BASE CLASSIFIERS AND MIXTURE OF EXP
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7 Bagging and Boosting 7.1 BAGGING
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BAGGING 205 Since there are only tw
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BAGGING 207 and d j,1 (x) ¼ d j,2
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BAGGING 209 5. Repeat steps (1) to
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BAGGING 211 Fig. 7.4 Error rates fo
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BOOSTING 213 HEDGE (b) Given: D¼f
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BOOSTING 215 shows how the probabil
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BOOSTING 217 Fig. 7.8 Testing error
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BOOSTING 219 into account by the cl
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BOOSTING 221 Fig. 7.10 example. Mar
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BIAS-VARIANCE DECOMPOSITION 223 7.3
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BIAS-VARIANCE DECOMPOSITION 225 the
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BIAS-VARIANCE DECOMPOSITION 227 dis
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WHICH IS BETTER: BAGGING OR BOOSTIN
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PROOF OF THE ERROR FOR AdaBoost (TW
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PROOF OF THE ERROR FOR AdaBoost (TW
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PROOF OF THE ERROR FOR AdaBoost (C
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238 MISCELLANEA for tree classifier
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240 MISCELLANEA This greedy algorit
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242 MISCELLANEA better than the fit
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244 MISCELLANEA 8.2 ERROR CORRECTIN
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246 MISCELLANEA Exhaustive Codes. D
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248 MISCELLANEA Fig. 8.2 Three impl
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250 MISCELLANEA TABLE 8.3 Possible
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252 MISCELLANEA to be the sum of al
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254 MISCELLANEA 8.3.1.3 Jaccard Ind
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256 MISCELLANEA tive definition of
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258 MISCELLANEA . Randomizing. Some
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260 MISCELLANEA Fig. 8.7 Stacked cl
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262 MISCELLANEA The generic cluster
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264 MISCELLANEA The ultimate goal o
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266 MISCELLANEA logicalA=A(i)==A(j)
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268 THEORETICAL VIEWS AND RESULTS T
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270 THEORETICAL VIEWS AND RESULTS F
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272 THEORETICAL VIEWS AND RESULTS T
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274 THEORETICAL VIEWS AND RESULTS T
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276 THEORETICAL VIEWS AND RESULTS F
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278 THEORETICAL VIEWS AND RESULTS C
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280 THEORETICAL VIEWS AND RESULTS T
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282 THEORETICAL VIEWS AND RESULTS 9
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284 THEORETICAL VIEWS AND RESULTS F
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286 THEORETICAL VIEWS AND RESULTS t
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288 THEORETICAL VIEWS AND RESULTS s
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290 THEORETICAL VIEWS AND RESULTS 9
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292 THEORETICAL VIEWS AND RESULTS F
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10 Diversity in Classifier Ensemble
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WHAT IS DIVERSITY? 297 Fig. 10.1 Di
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WHAT IS DIVERSITY? 299 Clearly orac
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MEASURING DIVERSITY IN CLASSIFIER E
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MEASURING DIVERSITY IN CLASSIFIER E
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MEASURING DIVERSITY IN CLASSIFIER E
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RELATIONSHIP BETWEEN DIVERSITY AND
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RELATIONSHIP BETWEEN DIVERSITY AND
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RELATIONSHIP BETWEEN DIVERSITY AND
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RELATIONSHIP BETWEEN DIVERSITY AND
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USING DIVERSITY 315 Breiman [214] d
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USING DIVERSITY 317 a member of the
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USING DIVERSITY 319 A Matlab functi
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USING DIVERSITY 321 Fig. 10.13 meth
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EQUIVALENCE BETWEEN DIVERSITY MEASU
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MATLAB CODE FOR SOME OVERPRODUCE AN
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MATLAB CODE FOR SOME OVERPRODUCE AN
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330 REFERENCES 13. G. T. Toussaint.
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332 REFERENCES 55. D. L. Wilson. As
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334 REFERENCES 97. D. W. Ruck, S. K
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336 REFERENCES 132. X. Lin, S. Yaco
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338 REFERENCES 169. R. A. Jacobs. M
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340 REFERENCES 206. R. Avnimelech a
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342 REFERENCES 244. E. Pȩkalska, M
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344 REFERENCES 279. K. Tumer and J.
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Index Bagging, 166, 203 nice, 211 p
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INDEX 349 codeword, 244 dichotomy,