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Machine+Learning+Neural+and+Statistical+Classification_Part1

Machine+Learning+Neural+and+Statistical+Classification_Part1...

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Machine Learning, Neural and Statistical Classification Editors: D. Michie, D.J. Spiegelhalter, C.C. Taylor February 17, 1994
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Contents 1 Introduction 1 1.1 INTRODUCTION 1 1.2 CLASSIFICATION 1 1.3 PERSPECTIVES ON CLASSIFICATION 2 1.3.1 Statistical approaches 2 1.3.2 Machine learning 2 1.3.3 Neural networks 3 1.3.4 Conclusions 3 1.4 THE STATLOG PROJECT 4 1.4.1 Quality control 4 1.4.2 Caution in the interpretations of comparisons 4 1.5 THE STRUCTURE OF THIS VOLUME 5 2 Classification 6 2.1 DEFINITION OF CLASSIFICATION 6 2.1.1 Rationale 6 2.1.2 Issues 7 2.1.3 Class definitions 8 2.1.4 Accuracy 8 2.2 EXAMPLES OF CLASSIFIERS 8 2.2.1 Fisher’s linear discriminants 9 2.2.2 Decision tree and Rule-based methods 9 2.2.3 k-Nearest-Neighbour 10 2.3 CHOICE OF VARIABLES 11 2.3.1 Transformations and combinations of variables 11 2.4 CLASSIFICATION OF CLASSIFICATION PROCEDURES 12 2.4.1 Extensions to linear discrimination 12 2.4.2 Decision trees and Rule-based methods 12
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ii [Ch. 0 2.4.3 Density estimates 12 2.5 A GENERAL STRUCTURE FOR CLASSIFICATION PROBLEMS 12 2.5.1 Prior probabilities and the Default rule 13 2.5.2 Separating classes 13 2.5.3 Misclassification costs 13 2.6 BAYES RULE GIVEN DATA 14 2.6.1 Bayes rule in statistics 15 2.7 REFERENCE TEXTS 16 3 Classical Statistical Methods 17 3.1 INTRODUCTION 17 3.2 LINEAR DISCRIMINANTS 17 3.2.1 Linear discriminants by least squares 18 3.2.2 Special case of two classes 20 3.2.3 Linear discriminants by maximum likelihood 20 3.2.4 More than two classes 21 3.3 QUADRATIC DISCRIMINANT 22 3.3.1 Quadratic discriminant - programming details 22 3.3.2 Regularisation and smoothed estimates 23 3.3.3 Choice of regularisation parameters 23 3.4 LOGISTIC DISCRIMINANT 24 3.4.1 Logistic discriminant - programming details 25 3.5 BAYES’ RULES 27 3.6 EXAMPLE 27 3.6.1 Linear discriminant 27 3.6.2 Logistic discriminant 27 3.6.3 Quadratic discriminant 27 4 Modern Statistical Techniques 29 4.1 INTRODUCTION 29 4.2 DENSITY ESTIMATION 30 4.2.1 Example 33 4.3 -NEAREST NEIGHBOUR 35 4.3.1 Example 36 4.4 PROJECTION PURSUIT CLASSIFICATION 37 4.4.1 Example 39 4.5 NAIVE BAYES 40 4.6 CAUSAL NETWORKS 41 4.6.1 Example 45 4.7 OTHER RECENT APPROACHES 46 4.7.1 ACE 46 4.7.2 MARS 47
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Sec. 0.0] iii 5 Machine Learning of Rules and Trees 50 5.1 RULES AND TREES FROM DATA: FIRST PRINCIPLES 50 5.1.1 Data fit and mental fit of classifiers 50 5.1.2 Specific-to-general: a paradigm for rule-learning 54 5.1.3 Decision trees 56 5.1.4 General-to-specific: top-down induction of trees 57 5.1.5 Stopping rules and class probability trees 61 5.1.6 Splitting criteria 61 5.1.7 Getting a “right-sized tree” 63 5.2 STATLOG’S ML ALGORITHMS 65 5.2.1 Tree-learning: further features of C4.5 65 5.2.2 NewID 65 5.2.3 67 5.2.4 Further features of CART 68 5.2.5 Cal5 70 5.2.6 Bayes tree 73 5.2.7 Rule-learning algorithms: CN2 73 5.2.8 ITrule 77 5.3 BEYOND THE COMPLEXITY BARRIER 79 5.3.1 Trees into rules 79 5.3.2 Manufacturing new attributes 80 5.3.3 Inherent limits of propositional-level learning 81 5.3.4 A human-machine compromise: structured induction 83 6 Neural Networks 84 6.1 INTRODUCTION 84 6.2 SUPERVISED NETWORKS FOR CLASSIFICATION 86 6.2.1 Perceptrons and Multi Layer Perceptrons 86 6.2.2 Multi Layer Perceptron structure and functionality 87 6.2.3
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