Machine Learning - DISCo

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1 248 MACHINE LEARNING Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford, England: Oxford University Press. Bisio, R., & Malabocchia, F. (1995). Cost estimation of software projects through case-based reasoning. In M. Veloso and A. Aamodt (Eds.), Lecture Notes in Artificial Intelligence (pp. 11-22). Berlin: Springer-Verlag. Broomhead, D. S., & Lowe, D. (1988). Multivariable functional interpolation and adaptive networks. Complex Systems, 2, 321-355. Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Infonnation Theory, 13,21-27. Duda, R., & Hart, P. (1973). Pattern classification and scene analysis. New York: John Wiley & Sons. Franke, R. (1982). Scattered data interpolation: Tests of some methods. Mathematics of Computation, 38, 181-200. Friedman, J., Bentley, J., & Finkel, R. (1977). An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software, 3(3), 209-226. Golding, A., & Rosenbloom, P. (1991). Improving rule-based systems through case-based reasoning. Proceedings of the Ninth National Conference on Artificial Intelligence (pp. 22-27). Cambridge: AAAI Pressme MIT Press. Hartman, E. J., Keller, J. D., & Kowalski, J. M. (1990). Layered neural networks with Gaussian hidden units as universal approximations. Neural Computation, 2(2), 210-215. Haton, J.-P., Keane, M., & Manago, M. (Eds.). (1995). Advances in case-based reasoning: Second European workshop. Berlin: Springer-Verlag. Kolodner, J. L. (1993). Case-Based Reasoning. San Francisco: Morgan Kaufmann. Moody, J. E., & Darken, C. J. (1989). Fast learning in networks of locally-tuned processing units. Neural Computation, 1(2), 281-294. Moore, A. W., & Lee, M. S. (1994). Efficient algorithms for minimizing cross validation error. Proceedings of the 11th International Conference on <strong>Machine</strong> <strong>Learning</strong>. San Francisco: Morgan Kaufmann. Poggio, T., & Girosi, F. (1990). Networks for approximation and learning. Proceedings of the IEEE, 78(9), 1481-1497. Powell, M. J. D. (1987). Radial basis functions for multivariable interpolation: A review. In Mason, J., & Cox, M. (Eds.). Algorithms for approximation (pp. 143-167). Oxford: Clarendon Press. Riesbeck, C., & Schank, R. (1989). Inside case-based reasoning. Hillsdale, NJ: Lawrence Erlbaum. Schank, R. (1982). Dynamic Memory. Cambridge, England: Cambridge University Press. Schank, R., Riesbeck, C., & Kass, A. (1994). Inside case-based explanation. Hillsdale, NJ: Lawrence Erlbaum. Shepard, D. (1968). A two-dimensional interpolation function for irregularly spaced data. Proceedings of the 23rd National Conference of the ACM (pp. 517-523). Stanfill, C., & Waltz, D. (1986). Toward memory-based reasoning. Communications of the ACM, 29(12), 1213-1228. Sycara, K., Guttal, R., Koning, J., Narasimhan, S., & Navinchandra, D. (1992). CADET: A casebased synthesis tool for engineering design. International Journal of Expert Systems, 4(2), 157-188. Veloso, M. M. (1992). Planning and learning by analogical reasoning. Berlin: Springer-Verlag. Veloso, M. M., & Aamodt, A. (Eds.). (1995). Case-based reasoning research and development. Lectwe Notes in Artificial Intelligence. Berlin: Springer-Verlag. Watson, I. (Ed.). (1995). Progress in case-based reasoning: First United Kingdom workshop. Berlin: Springer-Verlag. Wess, S., Althoff, K., & Richter, M. (Eds.). (1994). Topics in case-based reasoning. Berlin: Springer- Verlag.
CHAPTER GENETIC ALGORITHMS Genetic algorithms provide an approach to learning that is based loosely on simulated evolution. Hypotheses are often described by bit strings whose interpretation depends on the application, though hypotheses may also be described by symbolic expressions or even computer programs. The search for an appropriate hypothesis begins with a population, or collection, of initial hypotheses. Members of the current population give rise to the next generation population by means of operations such as random mutation and crossover, which are patterned after processes in biological evolution. At each step, the hypotheses in the current population are evaluated relative to a given measure of fitness, with the most fit hypotheses selected probabilistically as seeds for producing the next generation. Genetic algorithms have been applied successfully to a variety of learning tasks and to other optimization problems. For example, they have been used to learn collections of rules for robot control and to optimize the topology and learning parameters for artificial neural networks. This chapter covers both genetic algorithms, in which hypotheses are typically described by bit strings, and genetic programming, in which hypotheses are described by computer programs. 9.1 MOTIVATION Genetic algorithms (GAS) provide a learning method motivated by an analogy to biological evolution. Rather than search from general-to-specific hypotheses, or from simple-to-complex, GAS generate successor hypotheses by repeatedly mutating and recombining parts of the best currently known hypotheses. At each step,
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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
Page 209 and 210: 6.13 SUMMARY AND FURTHER READING Th
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Page 217 and 218: C Instance space X Where c and h di
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Page 225 and 226: C that contains every teachable con
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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
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Page 247 and 248: (i.e., on all axes in the Euclidean
Page 249 and 250: 8.3.1 Locally Weighted Linear Regre
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Page 259: A thorough discussion of radial bas
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Page 265 and 266: can then be represented by the foll
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Page 269 and 270: value of the target attribute c. Th
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Page 279 and 280: 9.6.2 Baldwin Effect Although Lamar
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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
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Page 295 and 296: 10.4 LEARNING FIRST-ORDER RULES In
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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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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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