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.