Offers frequent opportunities to practice techniques with control questions, exercises, thought experiments, and computer assignments.
Reinforces principles using well-selected toy domains and relevant real-world applications.
Provides additional material, including an instructor's manual with presentation slides, as well as answers to exercises.
Autorentext
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.
Klappentext
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.
Inhalt
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