Machine Learning - DISCo

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410 SUBJECT INDEX Normal distribution, 133, 139-140, 143, 151, 165 for noise, 167 in paired tests, 149 Occam's razor, 4, 65-66, 171 Offline learning systems, 385 One-sided bounds, 141, 144 Online learning systems, 385 Optimal brain damage approach, 122 Optimal code, 172 Optimal mistake bounds, 222-223 Optimal policy for selecting actions, 371-372 Optimization problems: explanation-based learning in, 325 genetic algorithms in, 256, 269 reinforcement learning in, 256 Output encoding in face recognition, 114-1 15 Output units, BACKPROPAGATION weight update rule for, 102-103 Overfitting, 123 in BACKPROPAGATION algorithm, 108, 11&111 in decision tree learning, 66-69, 76-77, 111 definition of, 67 Minimum Description Length principle and, 174 in neural network learning, 123 PAC learning, 203-207, 225, 226 of boolean conjunctions, 211-212 definition of, 206-207 training error in, 205 true error in, 204-205 Paired tests, 147-150, 152 Parallelization in genetic algorithms, 268 Partially learned concepts, 38-39 Partially observable states in reinforcement learning, 369-370 Perceptron training rule, 88-89, 94,95 Perceptrons, 86, 95, 96, 123 representation of boolean functions, VC dimension of, 219 weight update rule, 88-89, 94, 95 Perfect domain theory, 312-3 13 Performance measure, 6 Performance system, 11-12, 13 Philosophy, influence on machine learning, 4 Planning problems: PRODIGY in, 327 case-based reasoning in, 240-241 Policy for selecting actions, 370-372 Population evolution, in genetic algorithms, 260-262 Positive literal, 284, 285 Post-pruning: in decision tree learning, 68-69, 77, 281 in FOIL algorithm, 291 in LEARN-ONE-RULE, 281 Posterior probability, 155-156, 162 Power law of practice, 4 Power set, 40-42 Predicates, 284, 285 Preference bias, 64, 76, 77 Prior knowledge, 155-156, 336. See also Domain theory to augment search operators, 357-361 in Bayesian learning, 155 derivatives of target function, 346-356, 362 in explanation-based learning, 308-309 explicit, use in learning, 329 in human learning, 330 initialize-the-hypothesis approach, 339-346, 362 in PROLOG-EBG, 313 search alteration in inductive-analytical learning, 339-340, 362 weighting in inductive-analytical learning, 338, 362 Prioritized sweeping, 380 Probabilistic reasoning, 163 Probabilities: estimation of, 179-1 80 formulas, 159 maximum likelihood (ML) hypothesis for prediction of, 167-170 87-88 probability density, 165
Probability distribution, 133. See also Binomial distribution: Normal distribution approximately correct (PAC) learning. See PAC learning hxess control in manufacturing, 17 PRODIGY, 326-327, 330 Product rule, 159 ~ W L 300-302 , ~oL@% 275,302, 330 PROLOG-EBG, 313-321, 328-329 applications of, 325 deductive learning in, 321-322 definition of, 314 derivation of new features in, 320-321 domain theory in, 322 EBNN algorithm, comparison with, 356 explanation of training examples, 314-318 weakest preimage in, 329 inductive bias in, 322-323 inductive logic programming, comp'arison with, 322 limitations of, 329 perfect domain theory in, 313 prior knowledge in, 313 properties of, 319 regression process in, 316-318 Propositional rules: learning by sequential covering algorithms, 275 learning first-order rules, comparison with, 283 psychology, influence on machine learning, 4 Q function: in deterministic environments, 374 convergence of Q learning towards, 377-380 in nondeterministic environments, 381 convergence of Q learning towards, 382 Q learning algorithm, 372-376. See also Reinforcement learning advantages of, 386 in deterministic environments, 375 convergence, 377-380 training rule, 375-376 strategies in, 379 lookup table, neural network substitution for, 384 in nondeterministic environments, 381-383 convergence, 382-383 training rule, 382 updating sequence, 379 Query strategies, 37-38 Radial basis function networks, 231, 238-240, 245, 246, 247 advantages of, 240 Random variable, 133, 134, 137, 151 Randomized method, 150 Rank selection, 256 RBF networks. See Radial basis function networks RDT program, 303 Real-valued target function. See Continuous-valued target function Recurrent networks, 119-121. See also Neural networks, artificial Recursive rules, 284 learning by FOIL algorithm, 290 Reduced-error pruning, in decision tree learning, 69-71 REGRESS algorithm, 317-3 18 Regression, 236 in PROLOG-EBG, 316-381 Reinforcement learning, 367-387. See also Q learning algorithm applications of, 387 differences from other methods, 369-370 dynamic programming and, 380, 385-387 explanation-based learning and, 330 function approximation algorithms in, : 1 384-385 Relational descriptions, learning of, 302 Relative frequency, 282 Relative mistake bound for WEIGHTED-MAJORITY algorithm, 224-225 Residual, 236
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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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Hypothesis Space Hypotheses that ma
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Page 403 and 404: APPENDIX NOTATION Below is a summar
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