Elements of Machine Learning (1995)

Elements of Machine Learning by Pat Langley Preface 1. An overview of machine learning 1.1 The science of machine learning 1.2 Nature of the environment 1.3 Nature of representation and performance 1.4 Nature of the learning component 1.5 Five paradigms for machine learning 1.6 Summary of the chapter 2. The induction of logical conjunctions 2.1 General issues in logical induction 2.2 Nonincremental induction of logical conjunctions 2.3 Heuristic induction of logical conjunctions 2.4 Incremental induction of logical conjunctions 2.5 Incremental hill climbing for logical conjunctions 2.6 Genetic algorithms for logical concept induction 2.7 Summary of the chapter 3. The induction of threshold concepts 3.1 General issues for threshold concepts 3.2 Induction of criteria tables 3.3 Induction of linear threshold units 3.4 Induction of spherical threshold units 3.5 Summary of the chapter 4. The induction of competitive concepts 4.1 Instance-based learning 4.2 Learning probabilistic concept descriptions 4.3 Summary of the chapter 5. The construction of decision lists 5.1 General issues in disjunctive concept induction 5.2 Nonincremental learning using separate and conquer 5.3 Incremental induction using separate and conquer 5.4 Induction of decision lists through exceptions 5.5 Induction of competitive disjunctions 5.6 Instance-storing algorithms 5.7 Complementary beam search for disjunctive concepts 5.8 Summary of the chapter 6. Revision and extension of inference networks 6.1 General issues surrounding inference network 6.2 Extending an incomplete inference network 6.3 Inducing specialized concepts with inference networks 6.4 Revising an incorrect inference network 6.5 Network construction and term generation 6.6 Summary of the chapter 7. The formation of concept hierarchies 7.1 General issues concerning concept hierarchies 7.2 Nonincremental divisive formation of hierarchies 7.3 Incremental formation of concept hierarchies 7.4 Agglomerative formation of concept hierarchies 7.5 Variations on hierarchies into other structures 7.7 Summary of the chapter 8. Other issues in concept induction 8.1 Overfitting and pruning 8.2 Selecting useful features 8.3 Induction for numeric prediction 8.4 Unsupervised concept induction 8.5 Inducing relational concepts 8.6 Handling missing features 8.7 Summary of the chapter 9. The formation of transition networks 9.1 General issues for state-transition networks 9.2 Constructing finite-state transition networks 9.3 Forming recursive transition networks 9.4 Learning rules and networks for prediction 9.5 Summary of the chapter 10. The acquisition of search-control knowledge 10.1 General issues in search control 10.2 Reinforcement learning 10.3 Learning state-space heuristics from solution traces 10.4 Learning control knowledge for problem reduction 10.5 Learning control knowledge for means-ends analysis 10.6 The utility of search-control knowledge 10.7 Summary of the chapter 11. The formation of macro-operators 11.1 General issues related to macro-operators 11.2 The creation of simple macro-operators 11.3 The formation of flexible macro-operators 11.4 Problem solving by analogy 11.5 The utility of macro-operators 11.6 Summary of the chapter 12. Prospects for machine learning 12.1 Additional areas of machine learning 12.2 Methodological trends in machine learning 12.3 The future of machine learning References Index