An Introduction to Machine Learning

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Beschreibung

1 A Simple Machine-Learning Task 1

1.1 Training Sets and Classifiers.......................................................................... 1

1.2 Minor Digression: Hill-Climbing Search....................................................... 5

1.3 Hill Climbing in Machine Learning................................................................ 9

1.4 The Induced Classifier's Performance........................................................ 12

1.5 Some Di culties with Available Data......................................................... 14

1.6 Summary and Historical Remarks............................................................... 18

1.7 Solidify Your Knowledge.............................................................................. 19

2 Probabilities: Bayesian Classifiers 22

2.1 The Single-Attribute Case............................................................................. 22

2.2 Vectors of Discrete Attributes..................................................................... 27

2.3 Probabilities of Rare Events: Exploiting the Expert's Intuition............. 29

2.4 How to Handle Continuous Attributes....................................................... 35

2.5 Gaussian "Bell" Function: A Standard pdf................................................. 38

2.6 Approximating PDFs with Sets of Gaussians............................................ 40

2.7 Summary and Historical Remarks............................................................... 43

2.8 Solidify Your Knowledge.............................................................................. 46

3 Similarities: Nearest-Neighbor Classifiers 49

3.1 The k-Nearest-Neighbor Rule...................................................................... 49

3.2         Measuring Similarity...................................................................................... 52
3.3         Irrelevant Attributes and Scaling Problems............................................... 56
3.4         Performance Considerations........................................................................ 60
3.5         Weighted Nearest Neighbors....................................................................... 63
3.6         Removing Dangerous Examples.................................................................. 65
3.7         Removing Redundant Examples.................................................................. 68
3.8         Summary and Historical Remarks............................................................... 71
3.9         Solidify Your Knowledge.............................................................................. 72

4              Inter-Class Boundaries:

Linear and Polynomial Classifiers                                                                  75

4.1         The Essence..................................................................................................... 75

4.2         The Additive Rule: Perceptron Learning.................................................... 79

4.3         The Multiplicative Rule: WINNOW............................................................ 85

4.4         Domains with More than Two Classes........................................................ 88

4.5         Polynomial Classifiers..................................................................................... 91

4.6         Specific Aspects of Polynomial Classifiers................................................... 93

4.7         Numerical Domains and Support Vector Machines................................... 97

4.8         Summary and Historical Remarks.............................................................. 100

4.9         Solidify Your Knowledge............................................................................. 101

5              Artificial Neural Networks                                                                            105

5.1         Multilayer Perceptrons as Classifiers.......................................................... 105

5.2         Neural Network's Error............................................................................... 110

5.3         Backpropagation of Error........................................................................... 111

5.4         Special Aspects of Multilayer Perceptrons................................................ 117

5.5         Architectural Issues...................................................................................... 121

5.6         Radial Basis Function Networks................................................................. 123

5.7         Summary and Historical Remarks.............................................................. 126

5.8         Solidify Your Knowledge............................................................................. 128

6              Decision Trees                                                                                                    130

6.1         Decision Trees
6.2         Induction of Decision Trees........................................................................ 134

6.3         How Much Information Does an Attribute Convey?............................... 137

6.4         Binary Split of a Numeric Attribute.......................................................... 142

6.5         Pruning.......................................................................................................... 144

6.6         Converting the Decision Tree into Rules.................................................. 149

6.7         Summary and Historical Remarks.............................................................. 151

6.8         Solidify Your Knowledge............................................................................. 153

7              Computational Learning Theory                                                                  157

7.1         PAC Learning................................................................................................. 157
7.2         Examples of PAC Learnability.................................................................... 161

7.3         Some Practical and Theoretical Consequences......................................... 164

7.4         VC-Dimension and Learnability................................................................. 166

7.5         Summary and Historical Remarks.............................................................. 169

7.6         Exercises and Thought Experiments......................................................... 170

8              A Few Instructive Applications                                                                   173
8.1         Character Recognition................................................................................ 173
8.2         Oil-Spill Recognition.................................................................................... 177
8.3         Sleep Classification...................................................................................... 181
8.4         Brain-Computer Interface.......................................................................... 185
8.5         Medical Diagnosis........................................................................................ 189
8.6         Text Classification........................................................................................ 192
8.7         Summary and Historical Remarks............................................................ 194
8.8         Exercises and Thought Experiments........................................................ 195
9              Induction of Voting Assemblies                                                                  198
9.1         Bagging.......................................................................................................... 198
9.2         Schapire's Boosting..................................................................................... 201
9.3         Adaboost: Practical Version of Boosting................................................. <205
9.4         Variations on the Boosting Theme........................................................... 210
9.5         Cost-Saving Benefits of the Approach...................................................... 213
9.6         Summary and Historical Remarks............................................................ 215
9.7         Solidify Your Knowledge............................................................................ 216
10     Some Practical Aspects to Know About                                                   219
10.1     A Learner's Bias.......................................................................................... 219
10.2     Imbalanced Training Sets........................................................................... 223
10.3     Context-Dependent Domains..................................................................... 228
10.4     Unknown Attribute Values......................................................................... 231
10.5     Attribute Selection....................................................................................... 234
10.6     Miscellaneous............................................................................................... 237
10.7     Summary and Historical Remarks............................................................ 238
10.8     Solidify Your Knowledge............................................................................ 240
11     Performance Evaluation                                                                                 243
11.1     Basic Performance Criteria........................................................................ 243
11.2     Precision and Recall.................................................................................... 247
11.3     Other Ways to Measure Performance..................................................... 252
11.4     Learning Curves and Computational Costs............................................. 255
11.5     Methodologies of Experimental Evaluation............................................. 258
11.6     Summary and Historical Remarks............................................................ 261
11.7     Solidify Your Knowledge............................................................................ 263

12     Statistical Significance                                                                                     266
12.1     Sampling a Population................................................................................ 266
12.2     Benefiting from the Normal Distribution................................................ 271
12.3     Confidence Intervals................................................................................... 275
12.4     Statistical Evaluation of a Classifier.......................................................... 277
12.5     Another Kind of Statistical Evaluation..................................................... 280
12.6     Comparing Machine-Learning Techniques.............................................. 281
12.7     Summary and Historical Remarks............................................................ 284
12.8     Solidify Your Knowledge............................................................................ 285<
13     Induction in Multi-Label Domains                                                              287
13.1     Classical Machine Learning in
Multi-Label Domains................................................................................... 287
13.2     Treating Each Class Separately:
Binary Relevance......................................................................................... 290
13.3     Classifier Chains........................................................................................... 293
13.4     Another Possibility: Stacking..................................................................... 296
13.5     A Note on Hierarchically Ordered Classes............................................... 298
13.6     Aggregating the Classes.............................................................................. 301
13.7     Criteria for Performance Evaluation........................................................ 304
13.8     Summary and Historical Remarks............................................................ 307
13.9     Solidify Your Knowledge............................................................................ 308
14     Unsupervised Learning                                                                                    311
14.1     Cluster Analysis........................................................................................... 311
14.2     A Simple Algorithm: k-Means.................................................................... 315
14.3     More Advanced Versions of k-Means...................................................... 321
14.4     Hierarchical Aggregation............................................................................ 323
14.5     Self-Organizing Feature Maps: Introduction........................................... 326
14.6     Some Important Details.............................................................................. 329
14.7     Why Feature Maps?.................................................................................... 332
14.8     Summary and Historical Remarks............................................................ 334
14.9     Solidify Your Knowledge............................................................................ 335
15     Classifiers in the Form of Rulesets                                                           338
15.1     A Class Described By Rules....................................................................... 338
15.2     Inducing Rulesets by Sequential Covering............................................... 341
15.3     Predicates and Recursion.......................................................................... 344
15.4     More Advanced Search Operators............................................................ 347

15.5     Summary and Historical Remarks.............................................................. 349
15.6     Solidify Your Knowledge............................................................................ 350
16     The Genetic Algorithm<                                                                                    352<
16.1     The Baseline Genetic Algorithm................................................................ 352
16.2     Implementing the Individual Modules...................................................... 355
16.3     Why it Works............................................................................................... 359
16.4     The Danger of Premature Degeneration................................................. 362
16.5     Other Genetic Operators............................................................................ 364
16.6     Some Advanced Versions........................................................................... 367
16.7     Selections in k-NN Classifiers..................................................................... 370
16.8     Summary and Historical Remarks............................................................ 373
16.9     Solidify Your Knowledge............................................................................ 374
17     Reinforcement Learning                                                                                 376
17.1     How to Choose the Most Rewarding Action........................................... 376
17.2     States and Actions in a Game.................................................................... 379
17.3     The SARSA Approach................................................................................. 383
17.4     Summary and Historical Remarks............................................................ 384
17.5     Solidify Your Knowledge............................................................................ 384
Index                                                                                                                           395

Eigenschaften

Breite:	159
Gewicht:	557 g
Höhe:	237
Länge:	21
Seiten:	348
Sprachen:	Englisch
Autor:	Miroslav Kubat

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