Machine Learning and Data Mining

Foreword
Preface

Acknowledgements


Chapter 1: Introduction

1.1 THE NAME OF THE GAME
1.2 OVERVIEW OF MACHINE LEARNING METHODS
1.3 HISTORY OF MACHINE LEARNING
1.4 SOME EARLY SUCCESSES
1.5 APPLICATIONS OF MACHINE LEARNING
1.6 DATA MINING TOOLS AND STANDARDS
1.7 SUMMARY AND FURTHER READING


Chapter 2: Learning and Intelligence

2.1 WHAT IS LEARNING
2.2 NATURAL LEARNING
2.3 LEARNING, INTELLIGENCE, CONSCIOUSNESS
2.4 WHY MACHINE LEARNING
2.5 SUMMARY AND FURTHER READING


Chapter 3: Machine Learning Basics

3.1 BASIC PRINCIPLES
3.2 MEASURES FOR PERFORMANCE EVALUATION
3.3 ESTIMATING PERFORMANCE
3.4 *COMPARING PERFORMANCE OF MACHINE LEARNING ALGORITHMS
3.5 COMBINING SEVERAL MACHINE LEARNING ALGORITHMS
3.6 SUMMARY AND FURTHER READING


Chapter 4: Knowledge Representation

4.1 PROPOSITIONAL CALCULUS
4.2 *FIRST ORDER PREDICATE CALCULUS
4.3 DISCRIMINANT AND REGRESSION FUNCTIONS
4.4 PROBABILITY DISTRIBUTIONS
4.5 SUMMARY AND FURTHER READING


Chapter 5: Learning as Search

5.1 EXHAUSTIVE SEARCH
5.2 BOUNDED EXHAUSTIVE SEARCH (BRANCH AND BOUND)
5.3 BEST-FIRST SEARCH
5.4 GREEDY SEARCH
5.5 BEAM SEARCH
5.6 LOCAL OPTIMIZATION
5.7 GRADIENT SEARCH
5.8 SIMULATED ANNEALING
5.9 GENETIC ALGORITHMS
5.10 SUMMARY AND FURTHER READING


Chapter 6: Measures for Evaluating the Quality of Attributes

6.1 MEASURES FOR CLASSIFICATION AND RELATIONAL PROBLEMS
6.2 MEASURES FOR REGRESSION
6.3 **FORMAL DERIVATIONS AND PROOFS
6.4 SUMMARY AND FURTHER READING


Chapter 7: Data Preprocessing

7.1 REPRESENTATION OF COMPLEX STRUCTURES
7.2 DISCRETIZATION OF CONTINUOUS ATTRIBUTES
7.3 ATTRIBUTE BINARIZATION
7.4 TRANSFORMING DISCRETE ATTRIBUTES INTO CONTINUOUS
7.5 DEALING WITH MISSING VALUES
7.6 VISUALIZATION
7.7 DIMENSIONALITY REDUCTION
7.8 **FORMAL DERIVATIONS AND PROOFS
7.9 SUMMARY AND FURTHER READING


Chapter 8: *Constructive Induction

8.1 DEPENDENCE OF ATTRIBUTES
8.2 CONSTRUCTIVE INDUCTION WITH PRE-DEFINED OPERATORS
8.3 CONSTRUCTIVE INDUCTION WITHOUT PRE-DEFINED OPERATORS
8.4 SUMMARY AND FURTHER READING


Chapter 9: Symbolic Learning

9.1 LEARNING OF DECISION TREES
9.2 LEARNING OF DECISION RULES
9.3 LEARNING OF ASSOCIATION RULES
9.4 LEARNING OF REGRESSION TREES
9.5 *INDUCTIVE LOGIC PROGRAMMING
9.6 NAIVE AND SEMI-NAIVE BAYESIAN CLASSIFIER
9.7 BAYESIAN BELIEF NETWORKS
9.8 SUMMARY AND FURTHER READING


Chapter 10: Statistical Learning

10.1 NEAREST NEIGHBORS
10.2 DISCRIMINANT ANALYSIS
10.3 LINEAR REGRESSION
10.4 LOGISTIC REGRESSION
10.5 *SUPPORT VECTOR MACHINES
10.6 SUMMARY AND FURTHER READING


Chapter 11: Artificial Neural Networks

11.1 INTRODUCTION
11.2 TYPES OF ARTIFICIAL NEURAL NETWORKS
11.3 *HOPFIELD'S NEURAL NETWORK
11.4 *BAYESIAN NEURAL NETWORK
11.5 PERCEPTRON
11.6 RADIAL BASIS FUNCTION NETWORKS
11.7 **FORMAL DERIVATIONS AND PROOFS
11.8 SUMMARY AND FURTHER READING


Chapter 12: Cluster Analysis

12.1 INTRODUCTION
12.2 MEASURES OF DISSIMILARITY
12.3 HIERARCHICAL CLUSTERING
12.4 PARTITIONAL CLUSTERING
12.5 MODEL-BASED CLUSTERING
12.6 OTHER CLUSTERING METHODS
12.7 SUMMARY AND FURTHER READING


Chapter 13: **Learning Theory

13.1 COMPUTABILITY THEORY AND RECURSIVE FUNCTIONS
13.2 FORMAL LEARNING THEORY
13.3 PROPERTIES OF LEARNING FUNCTIONS
13.4 PROPERTIES OF INPUT DATA
13.5 CONVERGENCE CRITERIA
13.6 IMPLICATIONS FOR MACHINE LEARNING
13.7 SUMMARY AND FURTHER READING


Chapter 14: **Computational Learning Theory

14.1 INTRODUCTION
14.2 GENERAL FRAMEWORK FOR CONCEPT LEARNING
14.3 PAC LEARNING MODEL
14.4 VAPNIK-CHERVONENKIS DIMENSION
14.5 LEARNING IN THE PRESENCE OF NOISE
14.6 EXACT AND MISTAKE BOUNDED LEARNING MODELS
14.7 INHERENT UNPREDICTABILITY AND PAC-REDUCTIONS
14.8 WEAK AND STRONG LEARNING
14.9 SUMMARY AND FURTHER READING


Appendix A: *Definitions of some lesser known terms

A.1 COMPUTATIONAL COMPLEXITY CLASSES
A.2 ASYMPTOTIC NOTATION
A.3 SOME BOUNDS FOR PROBABILISTIC ANALYSIS
A.4 COVARIANCE MATRIX


References
Index