Introduction to Machine Learning
Published on 1. January 1993
Book
Paperback/Softback
298 pages
978-1-55860-037-9 (ISBN)
Description
A textbook suitable for undergraduate courses in machine learningand related topics, this book provides a broad survey of the field.Generous exercises and examples give students a firm grasp of theconcepts and techniques of this rapidly developing, challenging subject.
Introduction to Machine Learning synthesizes and clarifiesthe work of leading researchers, much of which is otherwise availableonly in undigested technical reports, journals, and conference proceedings.Beginning with an overview suitable for undergraduate readers, Kodratoffestablishes a theoretical basis for machine learning and describesits technical concepts and major application areas. Relevant logicprogramming examples are given in Prolog.
Introduction to Machine Learning is an accessible and originalintroduction to a significant research area.
Introduction to Machine Learning synthesizes and clarifiesthe work of leading researchers, much of which is otherwise availableonly in undigested technical reports, journals, and conference proceedings.Beginning with an overview suitable for undergraduate readers, Kodratoffestablishes a theoretical basis for machine learning and describesits technical concepts and major application areas. Relevant logicprogramming examples are given in Prolog.
Introduction to Machine Learning is an accessible and originalintroduction to a significant research area.
More details
Language
English
Place of publication
San Francisco
United States
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
Weight
520 gr
ISBN-13
978-1-55860-037-9 (9781558600379)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Yves Kodratoff
Introduction to Machine Learning
E-Book
06/2014
Morgan Kaufmann
€54.95
Available for download
Person
Content
1 Why Machine Learning and AI: The Contributions of AI to Learning Techniques
2 Theoretical Foundations for Machine Learning
3 Representation of Complex Knowledge by Clauses
4 Representation of Knowledge about Actions and the Addition of New Rules to a Knowledge Base
5 Learning by Doing
6 A Formal Presentation of Version Spaces
7 Explanation-Based Learning
8 Learning by Similarity Detection: The Empirical Approach
9 Learning by Similarity Detection: The 'Rational' Approach
10 Automatic Construction of Taxonomies: Techniques for Clustering
11 Debugging and Understanding in Depth: The Learning of Micro-Worlds
12 Learning by Analogy
Appendix 1 Equivalence Between Theorems and Clauses
Appendix 2 Synthesis of Predicates
Appendix 3 Machine Learning in Context
2 Theoretical Foundations for Machine Learning
3 Representation of Complex Knowledge by Clauses
4 Representation of Knowledge about Actions and the Addition of New Rules to a Knowledge Base
5 Learning by Doing
6 A Formal Presentation of Version Spaces
7 Explanation-Based Learning
8 Learning by Similarity Detection: The Empirical Approach
9 Learning by Similarity Detection: The 'Rational' Approach
10 Automatic Construction of Taxonomies: Techniques for Clustering
11 Debugging and Understanding in Depth: The Learning of Micro-Worlds
12 Learning by Analogy
Appendix 1 Equivalence Between Theorems and Clauses
Appendix 2 Synthesis of Predicates
Appendix 3 Machine Learning in Context