Machine Learning - DISCo

More documents

Recommendations

Info

LEARN-ONE-RULE algorithm: FOIL algorithm, comparison with, 287 ID3 algorithm, search comparison with, 277 rule performance in, 282 rule post-pruning in, 281 variations of, 279-280,286 Learning: human. See Human learning machine. See Machine learning Learning algorithms consistent learners, 162-163 design of, 9-11, 17 domain-independent, 336 error differences between two, 145-15 1 search of hypothesis space, 24 Learning problems, 2-5, 17 computational theory of, 201-202 in inductive-analytical learning, 337-338 Learning rate, 88, 91 Learning systems: design of, 5-14, 17 program modules, 11-1' : Least mean squares algori ,m. See LMS algorithm Least-squared error hypothesis: classifiers for, 198 gradient descent in, 167 maximum likelihood (ML) hypothesis and, 164-167 Leave-one-out cross-validation, 235 Legal case reasoning, case-based reasoning in, 240 LEMMA-ENUMERATOR algorithm, 324 Lifelong learning, 370 Line search, 119 Linear programming, as weight update algorithm, 95 Linearly separable sets, 86, 89, 95 LIST-THEN-ELLMINATE algorithm, 30 Literal, 284, 285 LMS algorithm, 11, 15 inductive bias of, 64 LMS weight update rule. See Delta rule Local method, 234 Locally weighted regression, 231, 236-238, 246 limitations of, 238 weight update rules in, 237-238 164-167 Logical constants, 284, 285 Logical terms, 284, 285 Logistic function, 96, 104 Lookup table: function approximation algorithms as substitute, 384 neural network as substitute, 384 Lower bound on sample complexity, 217-218 m-estimate of probability, 179-180, 198, 282 Machine learning, 15. See also entries beginning with Learning applications, 3, 17 definition of, 2 influence of other disciplines on, 4, 17 search of hypothesis space, 14-15, 18 Manufacturing process control, 17 MAP hypothesis. See Maximum a posteriori hypothesis MAP LEARNING algorithm, BRUTE-FORCE. See BRUTE-FORCE MAP LEARNING algorithm Markov decision processes (MDP), 370, 387 applications of, 386 MARKUS, 302 MARVIN, 302 Maximally general hypotheses, computation by CANDIDATE- ELIMINATION algorithm, 31, 46 Maximally specific hypotheses: computation by CANDIDATE- ELIMINATION algorithm, 31, 46 computation by FIND-S algorithm, 26-28, 62-63 Maximum a posteriori (MAP) hypothesis, 157, 197. See also BRUTE-FORCE MAP LEARNING algorithm naive Bayes classifier and, 178 output of consistent learners, 162-163 Maximum likelihood (ML) hypothesis, 157 EM algorithm search for, 194-195 least-squared error hypothesis and, - -
prediction of probabilities with, 167-170 MDP. See Markov decision processes Mean error, 143 Mean value, 133, 136 Means-ends planner, 326 Mechanical design, case-based reasoning in, 240-244 Medical diagnosis: attribute selection measure, 76 Bayes theorem in, 157-158 META-DENDRAL, 302 MFOIL, 302 Minimum Description Length principle, 66,69, 171-173, 197, 198 in decision tree learning, 173-174 in inductive logic programming, 292-293 MIS, 302 Mistake-bound learning, 202, 220, 226 in CANDIDATE-ELIMINATION algorithm, 221-222 in FIND-S algorithm, 220-221 in HALVING algorithm, 221-222 in LIST-THEN-ELIMINATE algorithm, 221-222 in WEIGHTED-MAJORITY algorithm, 224-225 Mistake bounds, optimal. See Optimal mistake bounds ML hypothesis. See Maximum likelihood hypothesis Momentum, addition to BACKPROPAGATION algorithm, 100, 104 More-general-than partial ordering, 24-28, 46 in CANDIDATE-ELIMINATION algorithm, 29 in FIND-S algorithm, 26-28 O-subsumption, entailment, and, 299-300 in version spaces, 31 Multilayer feedforward networks BACKPROPAGATION algorithm in, 95-101 function representation in, 105-106, 115 representation of decision surfaces, 96 training of multiple networks, 105 VC dimension of, 218-220 Naive Bayes classifier, 154-155, 177-179, 197 Bayesian belief network, comparison with, 186 maximum a posteriori (MAP) hypothesis and, 178 use in text classification, 180-184 Naive Bayes learner. See Naive Bayes classifier Negation-as-failure strategy, 279, 319, 321n Negative literal, 284, 285 Neural network learning, 81-124. See also BACKPROPAGATION algorithm; CASCADE-CORRELATION algorithm, EBNN algorithm, KBANN algorithm, TANGENTPROP algorithm applications of, 83, 85 in face recognition, 113 cross-validation in, 11 1-1 12 decision tree learning, comparison with, 85 discovery of hidden layer representations in, 107 overfitting in, 123 in Q learning, 384 representation in, 82-83, 84, 105-106 Neural networks, artificial, 81-124. See also Multilayer feedforward networks; Radial basis function networks; Recurrent networks biological neural networks, comparison with, 82 creation by KBANN algorithm, 342-343 VC dimension of, 218-220 Neural networks, biological, 82 Neurobiology, influence on machine learning, 4, 82 New features: derivation in BACKPROPAGATION algorithm, 106-109, 123 derivation in explanation-based learning, 320-321 NEWSWEEDER system, 183-184 Nondeterministic environments, Q learning Mutation operator, 252, 253, 255, 257, 262 in, 381-383
Page 2 and 3:
Machine Learning Tom M. Mitchell Pr
Page 4 and 5:
xvi PREFACE A third principle that
Page 13 and 14:
CHAPTER INTRODUCTION Ever since com
Page 15 and 16:
CHAPTER 1 INTRODUCITON 3 0 Learning
Page 17 and 18:
CHAFTlB 1 INTRODUCTION 5 that allow
Page 19 and 20:
1.2.2 Choosing the Target Function
Page 21 and 22:
CHAPTER I INTRODUCTION 9 x5: the nu
Page 23 and 24:
Thus, we seek the weights, or equiv
Page 25 and 26:
could involve creating board positi
Page 27 and 28:
the available training examples. Th
Page 29 and 30:
0 Chapter 10 covers algorithms for
Page 31 and 32:
ination of board features of your c
Page 33 and 34:
CHAFER 2 CONCEm LEARNING AND THE GE
Page 35 and 36:
When learning the target concept, t
Page 37 and 38:
CHAPTER 2 CONCEPT LEARNING AND THE
Page 39 and 40:
is needed. In the general case, as
Page 41 and 42:
2.5 VERSION SPACES AND THE CANDIDAT
Page 43 and 44:
{ 1 FIGURE 2.3 A version space w
Page 45 and 46:
CHAPTER 2 CONCEET LEARNJNG AND THE
Page 47 and 48:
C H m R 2 CONCEPT LEARNING AND THE
Page 49 and 50:
CH.4PTF.R 2 CONCEFT LEARNING AND TH
Page 51 and 52:
Page 53 and 54:
the power set of X? In general, the
Page 55 and 56:
CHAFI%R 2 CONCEPT LEARNING AND THE
Page 57 and 58:
CHAPTER 2 CONCEPT. LEARNING AND THE
Page 59 and 60:
CHAFTER 2 CONCEPT LEARNING AND THE
Page 61 and 62:
Page 63 and 64:
Hirsh, H. (1991). Theoretical under
Page 65 and 66:
CHAPTER 3 DECISION TREE LEARNING 53
Page 67 and 68:
CHAPTER 3 DECISION TREE LEARMNG 55
Page 69 and 70:
Page 71 and 72:
High wx CHAPTER 3 DECISION TREE LEA
Page 73 and 74:
{Dl, D2, ..., Dl41 P+S-I Which attr
Page 75 and 76:
3.6 INDUCTIVE BIAS IN DECISION TREE
Page 77 and 78:
3.6.2 Why Prefer Short Hypotheses?
Page 79 and 80:
easonable strategy, in fact it can
Page 81 and 82:
Although the first of these approac
Page 83 and 84:
multiple ways, then averaging the r
Page 85 and 86:
Page 87 and 88:
Page 89 and 90:
CHAFER 3 DECISION TREE LEARNING 77
Page 91 and 92:
C m 3 DECISION TREE LEARNING 79 Fay
Page 93 and 94:
CHAPTER ARTIFICIAL NEURAL NETWORKS
Page 95 and 96:
at normal speeds on public highways
Page 97 and 98:
~t is also applicable to problems f
Page 99 and 100:
FIGURE 43 A perceptron. A single pe
Page 101 and 102:
this example. Notice the training r
Page 103 and 104:
Gradient descent search determines
Page 105 and 106:
- - CHAF'l'ER 4 ARTIFICIAL NEURAL N
Page 107 and 108:
C H m R 4 ARTIFICIAL NEURAL NETWORK
Page 109 and 110:
CHAPTER 4 ARTIFICIAL NEURAL NETWORK
Page 111 and 112:
Page 113 and 114:
Page 115 and 116:
and combining this with Equations (
Page 117 and 118:
Page 119 and 120:
Inputs Outputs Input 10000000 0 100
Page 121 and 122:
FIGURE 4.8 Learning the 8 x 3 x 8 N
Page 123 and 124:
Page 125 and 126:
30 x 32 resolution input images lef
Page 127 and 128:
left, (0,1,0,O) to encode a face lo
Page 129 and 130:
close to zero, as the bright face w
Page 131 and 132:
4.8.2 Alternative Error Minimizatio
Page 133 and 134:
Page 135 and 136:
BACKPROPAGATION searches the space
Page 137 and 138:
4.6. Explain informally why the del
Page 139 and 140:
Mitchell, T. M., & Thrun, S. B. (19
Page 141 and 142:
the impact of possible pruning step
Page 143 and 144:
Here the notation Pr denotes that t
Page 145 and 146:
a A random variable can be viewed a
Page 147 and 148:
5.3.2 The Binomial Distribution A g
Page 149 and 150:
In case the random variable Y is go
Page 151 and 152:
which is wide enough to contain 95%
Page 153 and 154:
intervals for discrete-valued hypot
Page 155 and 156:
Then as n + co, the distribution go
Page 157 and 158:
we might observe this difference in
Page 159 and 160:
1. Partition the available data Do
Page 161 and 162:
perfectly fits the form of the abov
Page 163 and 164:
CHAFER 5 EVALUATING HYPOTHESES 151
Page 165 and 166:
Geman, S., Bienenstock, E., & Dours
Page 167 and 168:
CHAFER 6 BAYESIAN LEARNING 155 U Fo
Page 169 and 170:
CHAPTER 6 BAYESIAN LEARNING 157 As
Page 171 and 172:
. - Product rule: probability P(A A
Page 173 and 174:
CHAPTER 6 BAYESIAN LEARNING 161 Rec
Page 175 and 176:
CHAPTER 6 BAYESIAN LEARNING 163 of
Page 177 and 178:
CHAPTER 6 BAYESIAN LEARNING 165 a l
Page 179 and 180:
CHAPTER 6 BAYESIAN LEARNING 167 the
Page 181 and 182:
CHAPTER 6 BAYESIAN LEARNING 169 doe
Page 183 and 184:
CIUPlER 6 BAYESIAN LEARNING 171 sub
Page 185 and 186:
CHAPTER 6 BAYESIAN LEARNING 173 0 T
Page 187 and 188:
CHAFER 6 BAYESIAN LEARNING 175 the
Page 189 and 190:
6.9 NAIVE BAYES CLASSIFIER CHAPTJZR
Page 191 and 192:
CHAETER 6 BAYESIAN LEARNING 179 Sim
Page 193 and 194:
-a- CHAPTER 6 BAYESIAN LEARNING 181
Page 195 and 196:
CHAPTER 6 BAYESIAN LEARNING 183 Exa
Page 197 and 198:
CHAPTER 6 BAYESIAN LEARNING 185 In
Page 199 and 200:
C H m R 6 BAYESIAN LEARNING 187 Fir
Page 201 and 202:
CHAPTER 6 BAYESIAN LEARNING 189 For
Page 203 and 204:
CHAPTER 6 BAYESIAN LEARNING 191 ord
Page 205 and 206:
CHAPTER 6 BAYESIAN LEARNING 193 max
Page 207 and 208:
CHAPTER 6 BAYESIAN LEARNING 195 Ste
Page 209 and 210:
6.13 SUMMARY AND FURTHER READING Th
Page 211 and 212:
CHAPTER 6 BAYESIAN LEARNING 199 pro
Page 213 and 214:
CHAPTER COMPUTATIONAL LEARNING THEO
Page 215 and 216:
CHAPTER 7 COMPUTATIONAL LEARNING TH
Page 217 and 218:
C Instance space X Where c and h di
Page 219 and 220:
usually more concerned with the num
Page 221 and 222:
are drawn. Haussler (1988) provides
Page 223 and 224:
CHAPTER 7 COMPUTATIONAL LEARNING TH
Page 225 and 226:
C that contains every teachable con
Page 227 and 228:
Instance space X FIGURE 73 A set of
Page 229 and 230:
can show that it is at least 3, as
Page 231 and 232:
The following theorem bounds the VC
Page 233 and 234:
FIND-S: C CHAPTER 7 COMPUTATIONAL L
Page 235 and 236:
We define the optimal mistake bound
Page 237 and 238:
log' Rearranging terms yields which
Page 239 and 240:
Current research on computational l
Page 241 and 242:
(b) You now draw a new set of 100 i
Page 243 and 244:
CHAPTER 8 INSTANCE-BASED LEARNING 2
Page 245 and 246:
Page 247 and 248:
(i.e., on all axes in the Euclidean
Page 249 and 250:
8.3.1 Locally Weighted Linear Regre
Page 251 and 252:
C H m R 8 INSTANCE-BASED LEARNING 2
Page 253 and 254:
CHAPTER 8 INSTANCEBASED LEARNING 24
Page 255 and 256:
CHAPTER 8 INSTANCE-BASED LEARMNG 24
Page 257 and 258:
Page 259 and 260:
A thorough discussion of radial bas
Page 261 and 262:
CHAPTER GENETIC ALGORITHMS Genetic
Page 263 and 264:
Fitness: A function that assigns an
Page 265 and 266:
can then be represented by the foll
Page 267 and 268:
the crossover point n is chosen at
Page 269 and 270:
value of the target attribute c. Th
Page 271 and 272:
In the above experiment, the two ne
Page 273 and 274:
The second step above follows from
Page 275 and 276:
FIGURE 9.2 Crossover operation appl
Page 277 and 278:
0 (DU x y) (do until), which execut
Page 279 and 280:
9.6.2 Baldwin Effect Although Lamar
Page 281 and 282:
iteration, the most fit members of
Page 283 and 284:
Belew, R. K., & Mitchell, M. (Eds.)
Page 285 and 286:
Tumey, P. D., Whitley, D., & Anders
Page 287 and 288:
As an example of first-order rule s
Page 289 and 290:
10.2.1 General to Specific Beam Sea
Page 291 and 292:
A few remarks on the LEARN-ONE-RULE
Page 293 and 294:
decisions regarding preconditions o
Page 295 and 296:
10.4 LEARNING FIRST-ORDER RULES In
Page 297 and 298:
Every well-formed expression is com
Page 299 and 300:
eralize the current disjunctive hyp
Page 301 and 302:
Here let us also make the closed wo
Page 303 and 304:
In the case of noise-free training
Page 305 and 306:
(e.g., neural network) fits the dat
Page 307 and 308:
C : KnowMaterial v -Study C: KnowMa
Page 309 and 310:
CHAPTER 10 LEARNING SETS OF RULES 2
Page 311 and 312:
In contrast, the generate-and-test
Page 313 and 314:
which enable the user to specify th
Page 315 and 316:
Wrobel (1992) use rule schemata in
Page 317 and 318:
De Raedt, L., & Bruynooghe, M. (199
Page 319 and 320:
CHAPTER ANALYTICAL LEARNING Inducti
Page 321 and 322:
What is interesting about this ches
Page 323 and 324:
Given: rn Instance space X: Each in
Page 325 and 326:
theories to automatically improve p
Page 327 and 328:
Explanation: Training Example: FIGU
Page 329 and 330:
FIGURE 11.3 Computing the weakest p
Page 331 and 332:
example that is not yet covered by
Page 333 and 334:
contain no description of such a pr
Page 335 and 336:
additional detail that must be cons
Page 337 and 338:
CHAPTER 11 ANALYTICAL LEARNING 325
Page 339 and 340:
explaining to itself the reason for
Page 341 and 342:
that fits the learner's prior knowl
Page 343 and 344:
Height (Joe, Short) Height(Sue, Sho
Page 345 and 346:
CHAF'TER 11 ANALYTICAL LEARNING 333
Page 347 and 348:
CHAPTER 12 COMBINING INDUCTIVE AND
Page 349 and 350:
CHAF'TER 12 COMBINING INDUCTIVE AND
Page 351 and 352:
12.2.2 Hypothesis Space Search CAAP
Page 353 and 354:
- KBANN(Domain-Theory, Training_Exa
Page 355 and 356:
Page 357 and 358:
Page 359 and 360:
CHAF'TER 12 COMBINING INDUCTIVE AND
Page 361 and 362:
Page 363 and 364: Hypothesis Space Hypotheses that ma
Page 365 and 366: CHAFER 12 COMBINING INDUCTIVE AND A
Page 367 and 368: CHAPTER 12 COMBmG INDUCTIVE AND ANA
Page 369 and 370: CHAPTER 12 COMBINING INDUCTIVE AND
Page 371 and 372: CHAPTER 12 COMBINING INDUCTIVE AND
Page 373 and 374: Hypothesis Space 1 Hypotheses thatf
Page 375 and 376: y first order Horn clauses to learn
Page 377 and 378: Craven, M. W., & Shavlik, J. W. (19
Page 379 and 380: CHAPTER REINFORCEMENT LEARNING Rein
Page 381 and 382: has or does not have prior knowledg
Page 383 and 384: CHAPTER 13 REINFORCEMENT LEARNING 3
Page 387 and 388: CHAPTER 13 REINFORCEMENT LEARNJNG 3
Page 395 and 396: While Q learning and related reinfo
Page 397 and 398: a neural network reinforcement lear
Page 399 and 400: CHAFER 13 REINFORCEMENT LEARNING 38
Page 401 and 402: CHaPrER 13 REINFORCEMENT LEARNING 3
Page 403 and 404: APPENDIX NOTATION Below is a summar
Page 405 and 406: INDEXES
Page 407 and 408: in KBANN algorithm, 343-344 optimiz
Page 409 and 410: leave-one-out, 235 in neural networ
Page 411 and 412: extensions to, 258-259 ID5R algorit
Page 413: of LMS algorithm, 64 of ROTE-LEARNE
Page 417 and 418: Probability distribution, 133. See
Page 419 and 420: Split infomation, 73-74 Squashing f
show all

Machine Learning - DISCo

Create successful ePaper yourself

Delete template?

Save as template?