Introduction to machine learning

著者

書誌事項

Introduction to machine learning

Yves Kodratoff

Pitman, 1988

タイトル別名

Leçons d'apprentissage symbolique automatique

大学図書館所蔵 件 / 全13件

この図書・雑誌をさがす

注記

Bibliography: p. 287-294

Includes index

内容説明・目次

内容説明

This is a description of the state of the art in machine learning (ML) which includes many exercises and examples designed to give the reader a firm grasp of the concepts and techniques of this expanding subject. Although it develops the necessary theoretical basis of ML, the book begins with an overview suitable for readers lacking a theoretical background. The technical concepts of ML are illustrated using Prolog and the book shows how some of the complex problems inherent in using logic programming as knowledge representation may be handled. In addition, it contains the results of a number of leading ML researchers such as Mitchell, Michalski and Winston which are explored and presented for study. The work is intended to be a good starting point from which to learn about ML and should be particularly suitable for undergraduates taking computer science courses but also for those in the first year of an MSc or PhD course.

目次

  • Why machine learning and AI?
  • theoretical foundations for machine learning
  • representation of complex knowledge by clauses
  • representation of knowledge about actions
  • learning by doing - LEX, SAGE
  • the version spaces - a formal presentation
  • explanation-based learning - Mitchell's and DeJong's views of EBL
  • empirical learning by detection of similarities - a detailed example of Michalski's approach
  • rational learning by similarity detection - the "rational" approach
  • automatic construction of taxonomies
  • debugging and deep understanding
  • learning by analogy - Winston's approach to learning by analogy. Appendices: theoretical grounds of the equivalence between theorems and clauses - Herbrand's universe of a set of clauses, skolemization, semantic trees
  • synthesis of predicates - the role of program synthesis in ML, program synthesis from input-output sequences
  • ML in context - epistemological and social issues.

「Nielsen BookData」 より

詳細情報

ページトップへ