COPYRIGHT YEAR

2005

AUTHORS

Xin Yao, Yong Liu

TITLE

Machine Learning

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.

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25 TRIPLES      24 PREDICATES      22 URIs      14 LITERALS

Subject Predicate Object
1 book-chapters:426f62a2d25812b5228e992c50cdf846 sg:abstract 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.
2 sg:abstractRights OpenAccess
3 sg:bibliographyRights Restricted
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5 sg:bodyPdfRights Restricted
6 sg:chapterNumber Chapter 12
7 sg:copyrightHolder Springer Science+Business Media, LLC
8 sg:copyrightYear 2005
9 sg:ddsId Chap12
10 sg:doi 10.1007/0-387-28356-0_12
11 sg:esmRights OpenAccess
12 sg:hasBook books:ff079519b4b8095aad83919efd4f8db5
13 sg:hasContributingOrganization grid-institutes:grid.6572.6
14 sg:hasContribution contributions:707e3cc0e05c182d3f9b05d4057be045
15 contributions:ecaa9b108fe6de37df3a1757ca4b96a6
16 sg:language En
17 sg:license http://scigraph.springernature.com/explorer/license/
18 sg:metadataRights OpenAccess
19 sg:pageFirst 341
20 sg:pageLast 373
21 sg:scigraphId 426f62a2d25812b5228e992c50cdf846
22 sg:title Machine Learning
23 sg:webpage https://link.springer.com/10.1007/0-387-28356-0_12
24 rdf:type sg:BookChapter
25 rdfs:label BookChapter: Machine Learning
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