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11.4 INCREMENTAL LEARNING 435When the vertical axis representing time 0 can move left no farther, it stops <strong>and</strong>the time origin starts to increase from 0 to keep pace with the data coming inat the right. Thus when the chart is full it shows a window of the most recenttime units. The strip chart can be configured to alter the number of instancesshown on the x axis.This particular Knowledge Flow configuration can process input files of anysize, even ones that do not fit into the computer’s main memory. However, it alldepends on how the classifier operates internally. For example, although theyare incremental, many instance-based learners store the entire dataset internally.
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Data MiningPractical Machine Learni
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Publisher:Publishing Services Manag
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viFOREWORDThis book presents this n
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CONTENTSix4 Algorithms: The basic m
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CONTENTSxiGenerating good rules 202
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CONTENTSxiii8 Moving on: Extensions
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CONTENTSxv13 The command-line inter
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xviiiLIST OF FIGURESFigure 4.10 The
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xxLIST OF FIGURESFigure 10.13 Worki
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xxiiLIST OF TABLESTable 5.2 Confide
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xxivPREFACEalchemy. Instead, there
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xxviPREFACEwho interprets them, and
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xxviiiPREFACEin Section 6.3. We hav
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xxxPREFACEration. All who have work
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partIMachine Learning Toolsand Tech
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4 CHAPTER 1 | WHAT’S IT ALL ABOUT
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10 CHAPTER 1 | WHAT’S IT ALL ABOU
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chapter 2Input:Concepts, Instances,
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2.1 WHAT’S A CONCEPT? 43increase
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2.2 WHAT’S IN AN EXAMPLE? 45data
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2.2 WHAT’S IN AN EXAMPLE? 47Table
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2.3 WHAT’S IN AN ATTRIBUTE? 49Tab
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2.3 WHAT’S IN AN ATTRIBUTE? 51Not
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2.4 PREPARING THE INPUT 53cleaned u
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missing values in this dataset). Th
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dividing by the range between the m
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2.4 PREPARING THE INPUT 59a record
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chapter 3Output:Knowledge Represent
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3.2 DECISION TREES 63Alternatively,
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3.3 CLASSIFICATION RULES 65nated by
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3.3 CLASSIFICATION RULES 671abx = 1
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3.4 ASSOCIATION RULES 69yes, then i
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3.5 RULES WITH EXCEPTIONS 71If peta
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3.6 RULES INVOLVING RELATIONS 73ica
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Standard relations include equality
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PRP =-56.1+0.049 MYCT+0.015 MMIN+0.
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3.8 INSTANCE-BASED REPRESENTATION 7
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3.9 Clusters3.9 CLUSTERS 81When clu
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chapter 4Algorithms:The Basic Metho
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4.1 INFERRING RUDIMENTARY RULES 85F
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described overfitting-avoidance bia
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4.2 STATISTICAL MODELING 89Table 4.
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just as we calculated previously. A
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4.2 STATISTICAL MODELING 93Table 4.
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of a document. Instead, a document
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4.3 DIVIDE-AND-CONQUER: CONSTRUCTIN
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4.4 COVERING ALGORITHMS: CONSTRUCTI
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4.5 MINING ASSOCIATION RULES 113acc
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4.5 MINING ASSOCIATION RULES 115Tab
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4.5 MINING ASSOCIATION RULES 117whi
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4.6 LINEAR MODELS 119through the da
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4.6 LINEAR MODELS 121However, linea
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4.6 LINEAR MODELS 123n( i)i iÂ 1-x
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4.6 LINEAR MODELS 125Set all weight
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4.6 LINEAR MODELS 127While some ins
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4.7 INSTANCE-BASED LEARNING 129When
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4.7 INSTANCE-BASED LEARNING 131Figu
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4.8 CLUSTERING 137As we saw in Sect
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4.9 FURTHER READING 139can be updat
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4.9 FURTHER READING 141Bayes was an
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chapter 5Credibility:Evaluating Wha
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5.1 TRAINING AND TESTING 145of each
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ather than error rate, so this corr
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5.3 CROSS-VALIDATION 149mediate con
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5.4 OTHER ESTIMATES 151A single 10-
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90% used in 10-fold cross-validatio
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5.5 COMPARING DATA MINING METHODS 1
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5.6 PREDICTING PROBABILITIES 157In
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5.6 PREDICTING PROBABILITIES 159whe
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mental job expected of a loss funct
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y the total number of positives, wh
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5.7 COUNTING THE COST 165different
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5.7 COUNTING THE COST 167Table 5.6D
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5.7 COUNTING THE COST 169100%80%tru
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5.7 COUNTING THE COST 171should cho
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Different terms are used in differe
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5.7 COUNTING THE COST 1750.5Anormal
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5.8 EVALUATING NUMERIC PREDICTION 1
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5.9 THE MINIMUM DESCRIPTION LENGTH
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5.9 THE MINIMUM DESCRIPTION LENGTH
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5.10 APPLYING THE MDL PRINCIPLE TO
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5.11 FURTHER READING 185tion theory
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188 CHAPTER 6 | IMPLEMENTATIONS: RE
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chapter 7Transformations:Engineerin
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7.1 ATTRIBUTE SELECTION 287attribut
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7.1 ATTRIBUTE SELECTION 289and less
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tion—and it is much easier to und
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7.1 ATTRIBUTE SELECTION 293outlook
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7.1 ATTRIBUTE SELECTION 295the t-te
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7.2 DISCRETIZING NUMERIC ATTRIBUTES
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7.3 SOME USEFUL TRANSFORMATIONS 305
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7.4 AUTOMATIC DATA CLEANSING 313Int
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7.5 COMBINING MULTIPLE MODELS 315da
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7.5 COMBINING MULTIPLE MODELS 317pa
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7.5 COMBINING MULTIPLE MODELS 319mo
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7.5 COMBINING MULTIPLE MODELS 321Ra
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7.5 COMBINING MULTIPLE MODELS 323Ho
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7.5 COMBINING MULTIPLE MODELS 325Th
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7.5 COMBINING MULTIPLE MODELS 327of
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7.5 COMBINING MULTIPLE MODELS 329ou
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7.5 COMBINING MULTIPLE MODELS 331ev
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7.5 COMBINING MULTIPLE MODELS 333be
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7.5 COMBINING MULTIPLE MODELS 335Ta
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7.6 Using unlabeled data7.6 USING U
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7.6 USING UNLABELED DATA 339automat
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7.7 FURTHER READING 341deal with we
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7.7 FURTHER READING 343Domingos (19
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346 CHAPTER 8 | MOVING ON: EXTENSIO
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chapter 9Introduction to WekaExperi
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9.2 How do you use it?9.2 HOW DO YO
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chapter 10The ExplorerWeka’s main
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10.1 GETTING STARTED 371(a)(b)(c)Fi
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10.1 GETTING STARTED 373deviation.
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10.1 GETTING STARTED 375=== Run inf
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10.1 GETTING STARTED 377Doing it ag
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10.1 GETTING STARTED 379(a)(b)Figur
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10.2 EXPLORING THE EXPLORER 381(a)(
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10.2 EXPLORING THE EXPLORER 383(b)(
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Page 438 and 439: 10.4 LEARNING ALGORITHMS 405Table 1
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Page 456 and 457: 10.8 ATTRIBUTE SELECTION 423tribute
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Page 461 and 462: 428 CHAPTER 11 | THE KNOWLEDGE FLOW
Page 467: 434 CHAPTER 11 | THE KNOWLEDGE FLOW
Page 471 and 472: 438 CHAPTER 12 | THE EXPERIMENTER12
Page 473 and 474: 440 CHAPTER 12 | THE EXPERIMENTERsc
Page 475 and 476: 442 CHAPTER 12 | THE EXPERIMENTERal
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Page 482 and 483: chapter 13The Command-line Interfac
Page 484 and 485: 13.2 THE STRUCTURE OF WEKA 451Large
Page 486 and 487: The weka.classifiers packageThe cla
Page 488 and 489: 13.2 THE STRUCTURE OF WEKA 455Figur
Page 490 and 491: 13.3 COMMAND-LINE OPTIONS 457Table
Page 492: 13.3 COMMAND-LINE OPTIONS 459proced
Page 495 and 496: 462 CHAPTER 14 | EMBEDDED MACHINE L
Page 504 and 505: chapter 15Writing New Learning Sche
Page 506 and 507: 15.1 AN EXAMPLE CLASSIFIER 473packa
Page 508 and 509: 15.1 AN EXAMPLE CLASSIFIER 475m_Cla
Page 510 and 511: 15.1 AN EXAMPLE CLASSIFIER 477* @re
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486 REFERENCESAsmis, E. 1984. Epicu
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488 REFERENCESCavnar, W. B., and J.
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490 REFERENCESDrucker, H. 1997. Imp
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492 REFERENCESFreund, Y., and L. Ma
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494 REFERENCESHall, M., G. Holmes,
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496 REFERENCESKohavi, R. 1995a. A s
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498 REFERENCESMann, T. 1993. Librar
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500 REFERENCESHeckerman, H. Mannila
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502 REFERENCESStone, P., and M. Vel
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IndexAactivation function, 234acuit
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INDEX 507automatic filtering, 315av
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INDEX 509randomization, 320-321stac
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INDEX 511document classification, 9
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INDEX 513Fisher, R. A., 15flat file
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INDEX 515KK2, 278Kappa statistic, 1
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INDEX 517multiclass alternating dec
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INDEX 519predicting performance, 14
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INDEX 521sigmoid kernel, 219Simple
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INDEX 523unlabeled data, 337-341clu
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About the AuthorsIan H. Witten is a
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