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. © Springer Science+Business Media New York 2014.
| Original language | English |
|---|---|
| Title of host publication | Search Methodologies : Introductory Tutorials in Optimization and Decision Support Techniques, Second Edition |
| Editors | Edmund K. BURKE, Graham KENDALL |
| Publisher | Springer US |
| Chapter | 17 |
| Pages | 477-518 |
| Number of pages | 42 |
| Edition | 2nd |
| ISBN (Electronic) | 9781461469407 |
| ISBN (Print) | 9781461469407 |
| DOIs | |
| Publication status | Published - 2014 |
| Externally published | Yes |
Fingerprint
Dive into the research topics of 'Machine learning'. Together they form a unique fingerprint.Research output
- 7 Scopus Citations
- 1 Book Chapter
-
Machine learning
YAO, X. & LIU, Y., 2005, Search Methodologies : Introductory Tutorials in Optimization and Decision Support Techniques. BURKE, E. K. & KENDALL, G. (eds.). 1st ed. Springer New York, p. 341-373 33 p.Research output: Book Chapters | Papers in Conference Proceedings › Book Chapter › Research › peer-review
6 Link opens in a new tab Citations (Scopus)
Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver