Machine Learning - DISCo

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Hypotheses, estimation of accuracy (continued) bias and variance in estimate, 129, 151, 152 errors in, 129-131, 151 evaluation of, 128-129 justification of, in inductive vs. analytical learning, 334-336 representations of, 23 testing of, 144-145 Hypothesis space, 14-15 bias in, 40-42, 46, 129 finite, sample complexity for, 207-214, 225 infinite, sample complexity for, 214-220 VC dimension of, 214-217 Hypothesis space search by BACKPROPAGATION algorithm, 97, 106, 122-123 comparison with decision tree learning, 106 comparison with KBANN and TANGENTPROP algorithms, 350-35 1 by CANDIDATE-ELIMINATION algorithm, 64 in concept learning, 23-25, 46-47 constraints on, 302-303 by FIND-S algorithm, 27-28 by FOIL algorithm, 286-287, 357-361 by genetic algorithms, 250, 259 by gradient descent, 90-91 by ID3 algorithm, 60-62,64, 76 by KBANN algorithm, 346 by learning algorithms, 24 by LEARN-ONE-RULE, 277 in machine learning, 14-15, 18 use of prior knowledge, 339-340, 362 ID3 algorithm, 55-64,77 backtracking and, 62 CANDIDATE-ELIMINATION algorithm, comparison with, 61-62 choice of attributes in, 280-281 choice of decision tree, 63 cost-sensitive measures, 75-76 extensions to, 77. See also C4.5 algorithm inductive bias of, 63-64, 76 LEARN-ONE-RULE, search comparison with, 277 limitations of, 61-62 overfitting in, 67-68 search of hypothesis space, 60-62, 64, 76 sequential covering algorithms, comparison with, 280-281 specialized for concept learning, 56 use of information gain in, 58-60 ID5R algorithm, comparison with GABIL, 258 ILP. See Inductive logic programming Image encoding in face recognition, 114 Imperfect domain theory: in EBNN algorithm, 356 in explanation-based learning, 330 in FOCL algorithm, 360 in KBANN algorithm, 344-345 Incremental explanation methods, 328 Incremental gradient descent. See Stochastic gradient descent INCREMENTAL VERSION SPACE MERGING algorithm, 47 Inductive-analytical learning, 334-363 advantages of, 362 explanation-based learning and, 330 learning problem, 337-338 prior knowledge methods to alter search, 339-340,362 properties of ideal systems, 337 weighting of components in EBNN algorithm, 351-352,355 weighting prior knowledge in, 338 Inductive bias, 39-45, 137-138. See also Occam's razor; Preference bias; Restriction bias of BACKPROPAGATION algorithm, 106 bias-free learning, 40-42 of CANDIDATE-ELIMINATION algorithm, 43-46, 63-64 in decision tree learning, 63-66 definition of, 43 in explanation-based learning, 322-323 of FIND-S algorithm, 45 of ID3 algorithm, 63-64,76 of inductive learning algorithms, 42-46 of k-NEAREST NEIGHBOR algorithm, 234
of LMS algorithm, 64 of ROTE-LEARNER algorithm, 44-45 Inductive inference. See Inductive learning Inductive learning, 42, 307-308. See also Decision tree learning; Genetic algorithms; Inductive logic programming; Neural network learning analytical learning, comparison with, 310, 328-329, 334-336, 362 inductive bias in, 4246 Inductive learning hypothesis, 23 Inductive logic programming, 275,291 PROLOG-EBG, comparison with, 322 Information gain, 73 definition of, 57-58 in FOIL algorithm, 289 in ID3 algorithm, 55,5840 Information theory: influence on machine learning, 4 Minimum Description Length principle and, 172 Initialize-thehypothesis approach, 339-346 Bayesian belief networks in, 346 Instance-based learning, 230-247. See also Case-based reasoning; k-NEAREST NEIGHBOR algorithm; Locally weighted regression advantages, 245-246 case-based reasoning, comparison with other methods, 240 limitations of, 231 Inverse entailment, 292, 302 first-order, 297 generate-and-test beam search, comparison with, 299 in PROGOL, 300-302 Inverse resolution, 294-296, 302 first-order, 297-298 generate-and-test beam search, comparison with, 298-299 limitations of, 300 Inverted deduction, 291-293 J Jacobian, 354 Job-shop scheduling, genetic algorithms in, Joint probability distribution, in Bayesian belief networks, 185-187 k-fold cross-validation, 112, 147, 150 k-means problem, 191-193 derivation of EM algorithm for, 195-196 k-NEAREST NEIGHBOR algorithm, 231-233, 246 applications of, 234 cross-validation in, 235 decision tree and rule learning, comparison with, 235 distance-weighted, 233-234 inductive bias of, 234 memory indexing in, 236 k-term CNF expressions, 213-214 k-term DNF expressions, 213-214 K2 algorithm, 190-191 KBANN algorithm, 340-347, 362, 387 advantages of, 344 BACKPROPAGATION algorithm, comparison with, 344-345 BACKPROPAGATION weight update rule in, 343-344 hypothesis space search by BACKPROPAGATION and TANGENTPROP, comparison with, 350-35 1 limitations of, 345 prior knowledge in, 339 kd-tree, 236 Kernel function, 236, 238, 246 Kernel function, Gaussian. See Gaussian kernel function Knowledge-Based Artificial Neural Network (KBANN) algorithm. See KBANN algorithm Knowledge compilation, 320 Knowledge level learning, 323-325 Knowledge reformulation, 320 Lamarckian evolution, 266 Language bias. See Restriction bias Lazy explanation methods, 328 Lazy learning methods, comparison with eager learning, 244-245
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Machine Learning Tom M. Mitchell Pr
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xvi PREFACE A third principle that
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CHAPTER INTRODUCTION Ever since com
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CHAPTER 1 INTRODUCITON 3 0 Learning
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CHAFTlB 1 INTRODUCTION 5 that allow
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1.2.2 Choosing the Target Function
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CHAPTER I INTRODUCTION 9 x5: the nu
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Thus, we seek the weights, or equiv
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could involve creating board positi
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the available training examples. Th
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0 Chapter 10 covers algorithms for
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ination of board features of your c
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CHAFER 2 CONCEm LEARNING AND THE GE
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When learning the target concept, t
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CHAPTER 2 CONCEPT LEARNING AND THE
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is needed. In the general case, as
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2.5 VERSION SPACES AND THE CANDIDAT
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{ 1 FIGURE 2.3 A version space w
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CHAPTER 2 CONCEET LEARNJNG AND THE
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C H m R 2 CONCEPT LEARNING AND THE
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CH.4PTF.R 2 CONCEFT LEARNING AND TH
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the power set of X? In general, the
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CHAFI%R 2 CONCEPT LEARNING AND THE
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CHAPTER 2 CONCEPT. LEARNING AND THE
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CHAFTER 2 CONCEPT LEARNING AND THE
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Hirsh, H. (1991). Theoretical under
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CHAPTER 3 DECISION TREE LEARNING 53
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CHAPTER 3 DECISION TREE LEARMNG 55
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High wx CHAPTER 3 DECISION TREE LEA
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{Dl, D2, ..., Dl41 P+S-I Which attr
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3.6 INDUCTIVE BIAS IN DECISION TREE
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3.6.2 Why Prefer Short Hypotheses?
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easonable strategy, in fact it can
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Although the first of these approac
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multiple ways, then averaging the r
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CHAFER 3 DECISION TREE LEARNING 77
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C m 3 DECISION TREE LEARNING 79 Fay
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CHAPTER ARTIFICIAL NEURAL NETWORKS
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at normal speeds on public highways
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~t is also applicable to problems f
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FIGURE 43 A perceptron. A single pe
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this example. Notice the training r
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Gradient descent search determines
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- - CHAF'l'ER 4 ARTIFICIAL NEURAL N
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C H m R 4 ARTIFICIAL NEURAL NETWORK
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CHAPTER 4 ARTIFICIAL NEURAL NETWORK
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and combining this with Equations (
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Inputs Outputs Input 10000000 0 100
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FIGURE 4.8 Learning the 8 x 3 x 8 N
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30 x 32 resolution input images lef
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left, (0,1,0,O) to encode a face lo
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close to zero, as the bright face w
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4.8.2 Alternative Error Minimizatio
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BACKPROPAGATION searches the space
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4.6. Explain informally why the del
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Mitchell, T. M., & Thrun, S. B. (19
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the impact of possible pruning step
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Here the notation Pr denotes that t
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a A random variable can be viewed a
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5.3.2 The Binomial Distribution A g
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In case the random variable Y is go
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which is wide enough to contain 95%
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intervals for discrete-valued hypot
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Then as n + co, the distribution go
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we might observe this difference in
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1. Partition the available data Do
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perfectly fits the form of the abov
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CHAFER 5 EVALUATING HYPOTHESES 151
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Geman, S., Bienenstock, E., & Dours
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CHAFER 6 BAYESIAN LEARNING 155 U Fo
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CHAPTER 6 BAYESIAN LEARNING 157 As
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. - Product rule: probability P(A A
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CHAPTER 6 BAYESIAN LEARNING 161 Rec
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CHAPTER 6 BAYESIAN LEARNING 163 of
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CHAPTER 6 BAYESIAN LEARNING 165 a l
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CHAPTER 6 BAYESIAN LEARNING 167 the
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CHAPTER 6 BAYESIAN LEARNING 169 doe
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CIUPlER 6 BAYESIAN LEARNING 171 sub
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CHAPTER 6 BAYESIAN LEARNING 173 0 T
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CHAFER 6 BAYESIAN LEARNING 175 the
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6.9 NAIVE BAYES CLASSIFIER CHAPTJZR
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CHAETER 6 BAYESIAN LEARNING 179 Sim
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-a- CHAPTER 6 BAYESIAN LEARNING 181
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CHAPTER 6 BAYESIAN LEARNING 183 Exa
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CHAPTER 6 BAYESIAN LEARNING 185 In
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C H m R 6 BAYESIAN LEARNING 187 Fir
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CHAPTER 6 BAYESIAN LEARNING 189 For
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CHAPTER 6 BAYESIAN LEARNING 191 ord
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CHAPTER 6 BAYESIAN LEARNING 193 max
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CHAPTER 6 BAYESIAN LEARNING 195 Ste
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6.13 SUMMARY AND FURTHER READING Th
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CHAPTER 6 BAYESIAN LEARNING 199 pro
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CHAPTER COMPUTATIONAL LEARNING THEO
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CHAPTER 7 COMPUTATIONAL LEARNING TH
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C Instance space X Where c and h di
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usually more concerned with the num
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are drawn. Haussler (1988) provides
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CHAPTER 7 COMPUTATIONAL LEARNING TH
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C that contains every teachable con
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Instance space X FIGURE 73 A set of
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can show that it is at least 3, as
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The following theorem bounds the VC
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FIND-S: C CHAPTER 7 COMPUTATIONAL L
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We define the optimal mistake bound
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log' Rearranging terms yields which
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Current research on computational l
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(b) You now draw a new set of 100 i
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CHAPTER 8 INSTANCE-BASED LEARNING 2
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(i.e., on all axes in the Euclidean
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8.3.1 Locally Weighted Linear Regre
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C H m R 8 INSTANCE-BASED LEARNING 2
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CHAPTER 8 INSTANCEBASED LEARNING 24
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CHAPTER 8 INSTANCE-BASED LEARMNG 24
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A thorough discussion of radial bas
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CHAPTER GENETIC ALGORITHMS Genetic
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Fitness: A function that assigns an
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can then be represented by the foll
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the crossover point n is chosen at
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value of the target attribute c. Th
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In the above experiment, the two ne
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The second step above follows from
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FIGURE 9.2 Crossover operation appl
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0 (DU x y) (do until), which execut
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9.6.2 Baldwin Effect Although Lamar
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iteration, the most fit members of
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Belew, R. K., & Mitchell, M. (Eds.)
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Tumey, P. D., Whitley, D., & Anders
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As an example of first-order rule s
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10.2.1 General to Specific Beam Sea
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A few remarks on the LEARN-ONE-RULE
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decisions regarding preconditions o
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10.4 LEARNING FIRST-ORDER RULES In
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Every well-formed expression is com
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eralize the current disjunctive hyp
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Here let us also make the closed wo
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In the case of noise-free training
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(e.g., neural network) fits the dat
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C : KnowMaterial v -Study C: KnowMa
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CHAPTER 10 LEARNING SETS OF RULES 2
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In contrast, the generate-and-test
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which enable the user to specify th
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Wrobel (1992) use rule schemata in
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De Raedt, L., & Bruynooghe, M. (199
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CHAPTER ANALYTICAL LEARNING Inducti
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What is interesting about this ches
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Given: rn Instance space X: Each in
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theories to automatically improve p
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Explanation: Training Example: FIGU
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FIGURE 11.3 Computing the weakest p
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example that is not yet covered by
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contain no description of such a pr
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additional detail that must be cons
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CHAPTER 11 ANALYTICAL LEARNING 325
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explaining to itself the reason for
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that fits the learner's prior knowl
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Height (Joe, Short) Height(Sue, Sho
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CHAF'TER 11 ANALYTICAL LEARNING 333
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CHAPTER 12 COMBINING INDUCTIVE AND
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CHAF'TER 12 COMBINING INDUCTIVE AND
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12.2.2 Hypothesis Space Search CAAP
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- KBANN(Domain-Theory, Training_Exa
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CHAF'TER 12 COMBINING INDUCTIVE AND
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Page 403 and 404: APPENDIX NOTATION Below is a summar
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