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Introduction to machine learning /
A textbook suitable for undergraduate courses in machine learning.
| Clasificación: | Libro Electrónico |
|---|---|
| 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 |
MARC
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| 001 | SCIDIR_ocn893679687 | ||
| 003 | OCoLC | ||
| 005 | 20231120111829.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 141024s1988 caua ob 001 0 eng d | ||
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| 019 | |a 897645820 |a 931874287 | ||
| 020 | |a 9780080509303 |q (electronic bk.) | ||
| 020 | |a 0080509304 |q (electronic bk.) | ||
| 020 | |z 155860037X | ||
| 020 | |z 9781558600379 | ||
| 035 | |a (OCoLC)893679687 |z (OCoLC)897645820 |z (OCoLC)931874287 | ||
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| 050 | 4 | |a Q325.5 |b .K6213 1988eb | |
| 072 | 7 | |a COM |x 000000 |2 bisacsh | |
| 082 | 0 | 4 | |a 006.3/1 |2 23 |
| 100 | 1 | |a Kodratoff, Yves, |e author. | |
| 240 | 1 | 0 | |a Le�cons d'apprentissage symbolique automatique. |l English |
| 245 | 1 | 0 | |a Introduction to machine learning / |c Yves Kodratoff, Research Director, French National Scientific Research Council. |
| 264 | 1 | |a San Mateo, CA : |b Morgan Kaufmann, |c 1988. | |
| 300 | |a 1 online resource (v, 298 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references (pages 287-294) and index. | ||
| 500 | |a Translation of: Le�cons d'apprentissage symbolique automatique. | ||
| 520 | |a A textbook suitable for undergraduate courses in machine learning. | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 6 | |a Apprentissage automatique. |0 (CaQQLa)201-0131435 | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Machine learning |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a MACHINE LEARNING. |2 nasat | |
| 650 | 7 | |a TEACHING MACHINES. |2 nasat | |
| 650 | 7 | |a KNOWLEDGE REPRESENTATION. |2 nasat | |
| 650 | 7 | |a KNOWLEDGE BASES (ARTIFICIAL INTELLIGENCE) |2 nasat | |
| 650 | 7 | |a ARTIFICIAL INTELLIGENCE. |2 nasat | |
| 650 | 7 | |a LEARNING. |2 nasat | |
| 776 | 0 | 8 | |i Print version: |a Kodratoff, Yves. |s Le�cons d'apprentissage symbolique automatique. English. |t Introduction to machine learning |z 155860037X |w (DLC) 88046077 |w (OCoLC)20725307 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780080509303 |z Texto completo |