Machine Learning - DISCo

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CART system, 77 CASCADE-CORRELATION algorithm, 121-123 Case-based reasoning, 231, 240-244, 246, 247 advantages of, 243-244 applications of, 240 other instance-based learning methods, comparison with, 240 Causal knowledge, representation by Bayesian belief networks, 187 Central Limit Theorem, 133, 142-143, 167 Checkers learning program, 2-3,5-14, 387 algorithms for, 14 design, 13 as sequential control process, 369 Chemical mass spectroscopy, CANDIDATE-ELIMINATION algorithm in, 29 Chess learning program, 308-310 explanation-based learning in, 325 Chunking, 327, 330 CIGOL, 302 Circuit design, genetic programming in, 265-266 ' Circuit layout, genetic algorithms in, 256 Classification problems, 54 CLA~~IFYJAIVEBAYES-TEXT, 182-183 CLAUDIEN, 302 Clauses, 284, 285 CLS. See Concept <strong>Learning</strong> System Clustering, 191 CN2 algorithm, 278, 301 choice of attribute-pairs in, 280-281 Complexity, sample. See Sample complexity Computational complexity, 202 Computational complexity theory, influence on machine learning, 4 Computational learning theory, 201-227 Concept learning, 20-47 algorithms for, 47 Bayes theorem and, 158-163 definition of, 21 genetic algorithms in, 256 ID3 algorithm specialized for, 56 notation for, 22-23 search of hypothesis space, 23-25, 4-7 task design in, 21-22 Concept <strong>Learning</strong> System, 77 Concepts, partially learned, 38-39 Conditional independence, 185 in Bayesian belief networks, 186-187 Confidence intervals, 133, 138-141, 150, 151 for discrete-valued hypotheses, 131-132, 140-141 derivation of, 142-143 one-sided, 144, 145 Conjugate gradient method, 119 Conjunction of boolean literals, PAC learning of, 211-212 Consequent of Horn clause, 285 Consistent learners, 162-163 bound on sample complexity, 207-210, 225 equation for, 209 Constants, in logic, 284, 285 Constraint-based approaches in Bayesian belief networks, 191 Constructive induction, 292 Continuous functions, representation by feedforward networks, 105-106 Continuous-valued hypotheses, training error of, 89-90 Continuous-valued target function, 197 maximum likelihood (ML) hypothesis for, 164-167 Control theory, influence on machine learning, 4 Convergence of Q learning algorithm: in deterministic environments, 377-380, 386 in nondeterministic environments, 382-383, 386 Credit assignment, 5 Critic, 12, 13 Cross entropy, 170 minimization of, 118 Cross-validation, 111-1 12 for comparison of learning algorithms, 145-151 k-fold. See k-fold cross-validation in k-NEAREST NEIGHBOR algorithm, 235
leave-one-out, 235 in neural network learning, 11 1-1 12 Crossover mask, 254 Crossover operators, 252-254, 261, 262 single-point, 254, 261 two-point, 254, 257-258 uniform, 255 Crowding, 259, Cumulative reward, 371 Curse of dimensionality, 235 Data mining, 17 Decision tree learning, 52-77 algorithms for, 55, 77. See also C4.5 algorithm, ID3 algorithm applications of, 54 Bayesian learning, comparison with, 198 impact of pruning on accuracy, 128-129 inductive bias in, 63-66 k-NEAREST NEIGHBOR algorithm, comparison with, 235 Minimum Description Length principle in, 173-174 neural network learning, comparison with, 85 overfitting in, 6749, 76-77, 111 post-pruning in, 68-69, 77 reduced-error pruning in, 69-7 1 rule post-pruning in, 71-72, 281 search of hypothesis space, 60-62 by BACKPROPAGATION algorithm, comparison with, 106 Deductive learning, 321-322 Degrees of freedom, 147 Delayed learning methods, comparison with eager learning, 244-245 Delayed reward, in reinforcement learning, 369 Delta rule, 11, 88-90, 94, 99, 123 Demes, 268 Determinations, 325 Deterministic environments, Q learning algorithm for, 375 Directed acyclic neural networks. See Multilayer feedforward networks Discounted cumulative reward. 371 Discrete-valued hypotheses: confidence intervals for, 131-132, 140-141 derivation of, 142-143 training error of, 205 Discrete-valued target functions, approximation by decision tree learning, 52 Disjunctive sets of rules, learning by sequential covering algorithms, 275-276 Distance-weighted k-NEAREST NEIGHBOR algorithm, 233-234 Domain-independent learning algorithms, 336 Domain theory, 310, 329. See also imperfect domain theory; Perfect domain theory; Prior knowledge in analytical learning, 31 1-312 as KBANN neural network, 342-343 in PROLOG-EBG, 322 weighting of components in EBNN, 351-352 DYNA, 380 Dynamic programming: applications to reinforcement learning, 380 reinforcement learning and, 385-387 Eager learning methods, comparison with lazy learning, 244245 EBG algorithm, 313 EBNN algorithm, 351-356, 362, 387 other explanation-based learning methods, comparison with, 356 prior knowledge and gradient descent in, 339 TANGENTPROP algorithm in, 353 weighting of inductive-analytical components in, 355,362 EGGS algorithm, 313 EM algorithm, 190-196, 197 applications of, 191, 194 derivation of algorithm for k-means, 195-196 search for maximum likelihood (ML) hypothesis, 194-195
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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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CHAPTER 12 COMBINING INDUCTIVE AND
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Page 403 and 404: APPENDIX NOTATION Below is a summar
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