Machine learning

Xin YAO, Yong LIU

Research output: Book Chapters | Papers in Conference ProceedingsBook ChapterResearchpeer-review

5 Citations (Scopus)

Abstract

Machine learning is a very active sub-field of artificial intelligence concerned with the development of computational models of learning. Machine learning is inspired by the work in several disciplines: cognitive sciences, computer science, statistics, computational complexity, information theory, control theory, philosophy, and biology. Simply speaking, machine learning is learning by machine. From a computational point of view, machine learning refers to the ability of a machine to improve its performance based on previous results. From a biological point of view, machine learning is the study of how to create computers that will learn from experience and modify their activity based on that learning as opposed to traditional computers whose activity will not change unless the programmer explicitly changes it. © 2005 Springer Science+Business Media, LLC.
Original languageEnglish
Title of host publicationSearch Methodologies : Introductory Tutorials in Optimization and Decision Support Techniques
EditorsEdmund K. BURKE, Graham KENDALL
PublisherSpringer New York
Chapter12
Pages341-373
Number of pages33
Edition1st
ISBN (Electronic)9780387283562
ISBN (Print)9780387234601
DOIs
Publication statusPublished - 2005
Externally publishedYes

Bibliographical note

Introductory Tutorials in Search, Optimization and Decision Support Methodologies (INTROS)

Keywords

  • Neural Network
  • Machine Learning
  • Learning Algorithm
  • Connection Weight
  • Inductive Logic Programming

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  • Machine learning

    YAO, X. & LIU, Y., 2014, Search Methodologies : Introductory Tutorials in Optimization and Decision Support Techniques, Second Edition. BURKE, E. K. & KENDALL, G. (eds.). 2nd ed. Springer US, p. 477-518 42 p.

    Research output: Book Chapters | Papers in Conference ProceedingsBook ChapterResearchpeer-review

    4 Citations (Scopus)

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