An Introduction to Machine Learning
Springer International Publishing (Verlag)
978-3-319-63912-3 (ISBN)
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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.
Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for over 25 years. He has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of over 60 conferences and workshops, and is an editorial board member of three scientific journals. He is widely credited with co-pioneering research in two major branches of the discipline: induction of time-varying concepts and learning from imbalanced training sets. He also contributed to research in induction from multi-label examples, induction of hierarchically organized classes, genetic algorithms, and initialization of neural networks.
lt;b>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................................................................ 91.4 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....................................................... 352.5 Gaussian "Bell" Function: A Standard pdf................................................. 38
2.6 Approximating PDFs with Sets of Gaussians............................................ 402.7 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........................................................................ 603.5 Weighted Nearest Neighbors....................................................................... 63
3.6 Removing Dangerous Examples.................................................................. 65
3.7 Removing Redundant Examples.................................................................. 683.8 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.................................................................... 1617.3 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.................................................................................... 1778.3 Sleep Classification...................................................................................... 181
8.4 Brain-Computer Interface.......................................................................... 1858.5 Medical Diagnosis........................................................................................ 189
8.6 Text Classification........................................................................................ 1928.7 Summary and Historical Remarks............................................................ 1948.8 Exercises and Thought Experiments........................................................ 195
9 Induction of Voting Assemblies 1989.1 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...................................................... 2139.6 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......................................................................... 23110.5 Attribute Selection....................................................................................... 234
10.6 Miscellaneous............................................................................................... 23710.7 Summary and Historical Remarks............................................................ 238
10.8 Solidify Your Knowledge............................................................................ 24011 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............................................. 25811.6 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............................................................ 28412.8 Solidify Your Knowledge............................................................................ 285<
13 Induction in Multi-Label Domains 287
13.1 Classical Machine Learning inMulti-Label Domains................................................................................... 28713.2 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............................................................ 30713.9 Solidify Your Knowledge............................................................................ 308
14 Unsupervised Learning 31114.1 Cluster Analysis........................................................................................... 31114.2 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........................................... 32614.6 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.......................................................................... 34415.4 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................................................. 36216.5 Other Genetic Operators............................................................................ 364
16.6 Some Advanced Versions........................................................................... 36716.7 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................................................................................. 38317.4 Summary and Historical Remarks............................................................ 384
17.5 Solidify Your Knowledge............................................................................ 384
Index 395
"The presentation is mainly empirical, but precise and pedagogical, as each concept introduced is followed by a set of questions which allows the reader to check immediately whether they understand the topic. Each chapter ends with a historical summary and a series of computer assignments. ... this book could serve as textbook for an undergraduate introductory course on machine learning ... ." (Gilles Teyssière, Mathematical Reviews, April, 2017)
"This book describes ongoing human-computer interaction (HCI) research and practical applications. ... These techniques can be very useful in AR/VR development projects, and some of these chapters can be used as examples and guides for future research." (Miguel A. Garcia-Ruiz, Computing Reviews, January, 2019)
| Erscheinungsdatum | 19.09.2017 |
|---|---|
| Zusatzinfo | XIII, 348 p. 85 illus., 3 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 707 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
| Schlagworte | Artificial Intelligence • artificial intelligence (incl. robotics) • bayesian classifiers • Big Data/Analytics • Boosting • business mathematics & systems • Business mathematics & systems • Computational Intelligence • Computational Learning Theory • Computer Science • Data Mining • data mining and knowledge discovery • decision trees • Deep learning • Expert systems / knowledge-based systems • Genetic algorithms • linear and polynomial classifiers • machine learning • nearest neighbor classifier • Neural networks • Performance Evaluation • Reinforcement Learning • Robotics • Statistical Learning • time-varying classes, imbalanced representation • Unsupervised Learning |
| ISBN-10 | 3-319-63912-9 / 3319639129 |
| ISBN-13 | 978-3-319-63912-3 / 9783319639123 |
| Zustand | Neuware |
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