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Elements of machine learning

Author: Pat Langley
Publisher: San Francisco : Morgan Kaufmann, 1996.
Series: Morgan kauffmann Series in Machine Learning.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:

This book covers the main induction algorithms explored in the literature and presents them within a coherent theoretical framework that moves beyond traditional paradigm boundaries. Elements of  Read more...

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Document Type: Book
All Authors / Contributors: Pat Langley
ISBN: 1558603018 9781558603011
OCLC Number: 637007292
Description: 419 p. ; 23 cm.
Contents: 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;
Series Title: Morgan kauffmann Series in Machine Learning.
Responsibility: Pat Langley.

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