COPYRIGHT YEAR

1992

AUTHORS

Katharina Morik

TITLE

Applications of machine learning

ABSTRACT

During the last 10 years, machine learning has been successfully applied. Most often, the applications are confidential. Therefore, only few publications about real world applications exist. In this paper, an overview of machine learning applications is given with their scenarios. Some typical applications are described. Then, future directions of machine learning applications are proposed. It is argued that machine learning is now mature enough to be incorporated into standard systems as well as algorithms. The integration of learning modules into database and retrieval systems is one of the trends. Another trend is to automatically select an appropriate learning tool out of a toolbox. The third trend, which is even more challenging, no longer requires a distinguished learning module, but offers methods of machine learning to be applied by programmers in their regular system development. Software engineers of the future can use inductive techniques as they now use message passing, for instance. Then, any program can be enhanced by some learning ability.

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24 TRIPLES      24 PREDICATES      21 URIs      13 LITERALS

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1 book-chapters:c02f07e980148f23f96c519839e1c790 sg:abstract Abstract During the last 10 years, machine learning has been successfully applied. Most often, the applications are confidential. Therefore, only few publications about real world applications exist. In this paper, an overview of machine learning applications is given with their scenarios. Some typical applications are described. Then, future directions of machine learning applications are proposed. It is argued that machine learning is now mature enough to be incorporated into standard systems as well as algorithms. The integration of learning modules into database and retrieval systems is one of the trends. Another trend is to automatically select an appropriate learning tool out of a toolbox. The third trend, which is even more challenging, no longer requires a distinguished learning module, but offers methods of machine learning to be applied by programmers in their regular system development. Software engineers of the future can use inductive techniques as they now use message passing, for instance. Then, any program can be enhanced by some learning ability.
2 sg:abstractRights OpenAccess
3 sg:bibliographyRights Restricted
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6 sg:copyrightHolder Springer-Verlag
7 sg:copyrightYear 1992
8 sg:ddsId Chap4
9 sg:doi 10.1007/3-540-55546-3_31
10 sg:esmRights OpenAccess
11 sg:hasBook books:ef6383151092a80ba72b585a6d270281
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14 sg:language En
15 sg:license http://scigraph.springernature.com/explorer/license/
16 sg:metadataRights OpenAccess
17 sg:pageFirst 9
18 sg:pageLast 13
19 sg:scigraphId c02f07e980148f23f96c519839e1c790
20 sg:title Applications of machine learning
21 sg:webpage https://link.springer.com/10.1007/3-540-55546-3_31
22 rdf:type sg:BookChapter
23 rdfs:label BookChapter: Applications of machine learning
24 owl:sameAs http://lod.springer.com/data/bookchapter/978-3-540-47203-2_4
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