An Introduction to Machine Learning (Hardcover)

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