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Introduction to machine learning /
A textbook suitable for undergraduate courses in machine learning.
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés Francés |
| Publicado: |
San Mateo, CA :
Morgan Kaufmann,
1988.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Introduction to Machine Learning; Copyright Page; Table of Contents; Foreword and Acknowledgements; Chapter 1. Why Machine Learning and Artificial Intelligence? The Contribution of Artificial Intelligence to Learning Techniques; 1 HISTORICAL SKETCH; 2 VARIOUS SORTS OF LEARNING; Chapter 2. Theoretical Foundations for Machine Learning; 0 THEORETICAL FOUNDATIONS FOR THEORY-HATERS; 1 CLAUSES; 2 UNIFICATION; 3 RESOLUTION AND INFERENCE ON A SET OF CLAUSES; 4 THE KNUTH-BENDIX COMPLETION ALGORITHM; Chapter 3. Representation of Complex Knowledge by Clauses.
- 1 SOME EXAMPLES OF LOGICAL KNOWLEDGE REPRESENTATION2 THE TRANSFORMATION OF A GIVEN SENTENCE INTO A THEOREM; 3 REPRESENTATION OF A HIERARCHY DURING RESOLUTION; 4 REPRESENTATION BY TERNARY QUANTIFIED TREES
- Chapter 4. Representation of Knowledge About Actions and the Addition of New Rules to a Knowledge Base; 1 TRUTH MAINTENANCE; 2 PREDICATES IN ACTION MODE / CHECKING MODE; 3 MAIN RULES AND AUXILIARY RULES; 4 ORGANIZATION OF THE PROGRAM FOR THE REPRESENTATION OF ACTIONS; 5 THE CASE OF A NEW RULE HAVING THE SAME PREMISE AS AN OLD ONE; 6 NEW RULE MORE SPECIFIC THAN AN OLD ONE.
- 7 COMBINATION OF RULES8 GENERALIZATION OF RULES; 9 RULES FOR INFERENCE CONTROL; Chapter 5. Learning by Doing; 1 THE PROBLEM; 2 VERSION SPACES [Mitchell 1982] SEEN AS FOCUSSING; 3 APPLICATION TO RULE ACQUISITION; 4 LEARNING BY TRIAL AND ERROR; Chapter 6. A Formal Presentation of Version Spaces; 1 DIFFERENT DEFINITIONS OF GENERALIZATION; 2 VERSION SPACES; Chapter 7. Explanation-Based Learning; 1 INDUCTIVE VERSUS DEDUCTIVE LEARNING; 2 INTUITIVE PRESENTATION OF EBL; 3 GOAL REGRESSION; 4 EXPLANATION-BASED GENERALIZATION; 5 EXPLANATION-BASED LEARNING.
- Chapter 8. Learning by Similarity Detection The Empirical Approach1 GENERAL DEFINITIONS; 2 DESCRIPTION OF THE WHOLE EXAMPLE; 3 RECOGNITION; 4 SPARSENESS AND THE SELECTION CRITERIA FOR A GOOD FUNCTION; 5 THE PROCEDURE OF EMPTYING THE INTERSECTIONS
- 6 CREATION OF RECOGNITION FUNCTIONS; 7 RULES OF GENERALIZATION; 8 GENERATION OF RECOGNITION FUNCTIONS; 9 APPLICATION TO SOYBEAN PATHOLOGY; 10 APPLICATION TO AN ALGORITHM FOR CONCEPTUAL CLUSTERING; Chapter 9. Learning by Similarity Detection The 'Rational' Approach; 1 KNOWLEDGE REPRESENTATION.
- 2 DESCRIPTION OF A RATIONAL GENERALIZATION ALGORITHM3 USING AXIOMS AND IDEMPOTENCE; 4 A DEFINITION OF GENERALIZATION; 5 USE OF NEGATIVE EXAMPLES; CONCLUSION; Chapter 10. Automatic Construction of Taxonomies Techniques for Clustering; 1 A MEASURE OF THE AMOUNT OF INFORMATION ASSOCIATED WITH EACH DESCRIPTOR; 2 APPLICATION OF DATA ANALYSIS; 3 CONCEPTUAL CLUSTERING; Chapter 11. Debugging and Understanding in Depth The Learning of Micro-Worlds; 1 RECOGNITION OF MICRO-WORLDS; 2 DETECTION OF LIES; Chapter 12. Learning by Analogy; 1 A DEFINITION OF ANALOGY; 2 WINSTON'S USE OF ANALOGY.