Machine Learning - DISCo

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Holland, J. H. (1986). Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In R. Michalski, J. Carbonell, & T. Mitchell (Eds.), Machine learning: An artijicial intelligence approach (Vol. 2). San Mateo, CA: Morgan Kaufmann. Holland, J. H. (1989). Searching nonlinear functions for high values. Applied Mathematics and Computation, 32, 255-274. Janikow, C. Z. (1993). A knowledge-intensive GA for supervised learning. Machine Learning, 13, 189-228. Koza, J. (1992). Genetic programming: On the programming of computers by means of natural selection. Cambridge, MA: MIT Press. Koza, J. R. (1994). Genetic Programming 11: Automatic discovery of reusable programs. Cambridge, MA: The MIT Press. Koza, J. R., Bennett 111, F. H., Andre, D., & Keane, M. A. (1996). Four problems for which a computer program evolved by genetic programming is competitive with human performance. Proceedings of the 1996 IEEE International Conference on Evolutionary Computation (pp. 1-10). IEEE Press. Koza, J. R., Goldberg, D. E., Fogel, D. B., & Riolo, R. L. (Eds.). (1996b). Genetic programming 19%: Proceedings of the First Annual Conference. Cambridge, MA: MIT Press. Machine Learning: Special Issue on Genetic Algorithms (1988) 3:2-3, October. Machine Learning: Special Issue on Genetic Algorithms (1990) 5:4, October. Machine karning: Special Issue on Genetic Algorithms (1 993) l3:2,3, November. Mitchell, M. (1996). An introduction to genetic algorithms. Cambridge, MA: MIT Press. O'Reilly, U-M., & Oppacher, R. (1994). Program search with a hierarchical variable length representation: Genetic programming, simulated annealing, and hill climbing. In Y. Davidor et al. (Eds.), Parallel problem solving from nature-PPSN I11 (Vol. 866) (Lecture notes in computer science). Springer-Verlag. Rechenberg, I. (1965). Cybernetic solution path of an experimental problem. Ministry of aviation, Royal Aircraft Establishment, U.K. Rechenberg, I. (1973). Evolutionsstrategie: Optimierung technischer systeme nach prinzipien der biolgischen evolution. Stuttgart: Frommann-Holzboog. Schwefel, H. P. (1975). Evolutionsstrategie und numerische optimiemng (Ph.D. thesis). Technical University of Berlin. Schwefel, H. P. (1977). Numerische optimierung von computer-modellen mittels der evolutionsstrategie. Basel: Birkhauser. Schwefel, H. P. (1995). Evolution and optimum seeking. New York: John Wiley & Sons. Spiessens, P., & Manderick, B. (1991). A massively parallel genetic algorithm: Implementation and first analysis. Proceedings of the 4th International Conference on Genetic Algorithms (pp. 279-286). Smith, S. (1980). A learning system based on genetic adaptive algorithms (Ph.D. dissertation). Computer Science, University of Pittsburgh. Stender, J. (Ed.) (1993). Parallel genetic algorithms. Amsterdam: IOS Publishing. Tanese, R. (1989). Distributed genetic algorithms. Proceedings of the 3rd International Conference on Genetic Algorithms (pp. 434-439). Teller, A., & Veloso, M. (1994). PADO: A new learning architecture for object recognition. In K. Ikeuchi & M. Veloso (Eds.), Symbolic visual learning @p. 81-116). Oxford, England: Oxford Univ. Press. Turney, P. D. (1995). Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Al Research, 2, 369-409. http://www.cs.washington.edu/ research/jair/home.htmI.
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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
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 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
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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: Belew, R. K., & Mitchell, M. (Eds.)
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
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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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CHAFER 12 COMBINING INDUCTIVE AND A
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CHAPTER 12 COMBmG INDUCTIVE AND ANA
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Hypothesis Space 1 Hypotheses thatf
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y first order Horn clauses to learn
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Craven, M. W., & Shavlik, J. W. (19
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CHAPTER REINFORCEMENT LEARNING Rein
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has or does not have prior knowledg
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CHAPTER 13 REINFORCEMENT LEARNING 3
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CHAPTER 13 REINFORCEMENT LEARNJNG 3
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While Q learning and related reinfo
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a neural network reinforcement lear
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CHAFER 13 REINFORCEMENT LEARNING 38
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CHaPrER 13 REINFORCEMENT LEARNING 3
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APPENDIX NOTATION Below is a summar
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INDEXES
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in KBANN algorithm, 343-344 optimiz
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leave-one-out, 235 in neural networ
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extensions to, 258-259 ID5R algorit
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of LMS algorithm, 64 of ROTE-LEARNE
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prediction of probabilities with, 1
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Probability distribution, 133. See
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Split infomation, 73-74 Squashing f
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