| Autorzy | |
| Wydawnictwo | Springer, Berlin |
| Data wydania | |
| Liczba stron | 348 |
| Forma publikacji | książka w twardej oprawie |
| Język | angielski |
| ISBN | 9783319639123 |
| Kategorie | Wydobywanie danych |
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of "boosting," how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.
This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
An Introduction to Machine Learning
1.1 Training Sets and Classifiers.......................................................................... 1
1.2 Minor Digression: Hill-Climbing Search....................................................... 5
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
2.5 Gaussian "Bell" Function: A Standard pdf................................................. 38
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
3.5 Weighted Nearest Neighbors....................................................................... 63
3.6 Removing Dangerous Examples.................................................................. 65
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
7.3 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
8.3 Sleep Classification...................................................................................... 181
8.5 Medical Diagnosis........................................................................................ 189
8.8 Exercises and Thought Experiments........................................................ 195
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
9.6 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
10.5 Attribute Selection....................................................................................... 234
10.7 Summary and Historical Remarks............................................................ 238
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
11.6 Summary and Historical Remarks............................................................ 261
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
12.8 Solidify Your Knowledge............................................................................ 285<
13 Induction 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
13.9 Solidify Your Knowledge............................................................................ 308
14.2 A Simple Algorithm: k-Means.................................................................... 315
14.3 More Advanced Versions of k-Means...................................................... 321
14.4 Hierarchical Aggregation............................................................................ 323
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
15.4 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
16.5 Other Genetic Operators............................................................................ 364
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
17.4 Summary and Historical Remarks............................................................ 384
17.5 Solidify Your Knowledge............................................................................ 384
Index 395
801 777 223
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