COPYRIGHT YEAR

2004

AUTHORS

Flaviu Adrian Mărginean

TITLE

Soft Learning: A Conceptual Bridge between Data Mining and Machine Learning

ABSTRACT

It has been felt for some time that, despite employing different formalisms, being served by their own dedicated research communities and addressing distinct issues of practical interest, problems in Data Mining and Machine Learning connect through deep relationships. The paper [5] has taken a first step towards linking Data Mining and Machine Learning via Combinatorics by showing a correspondence between the problem of finding maximally specific sentences that are interesting in a database, the model of exact learning of monotone boolean functions in computational learning theory and the hyper-graph transversal problem in the combinatorics of finite sets. [5] summarises and concludes a series of valuable Data Mining research on fast discovery of association rules by the levelwise algorithm, series that includes [1, 4, 11]. Intuitively, a Data Mining task may consist of finding many weak predictors in a hypothesis space whereas in Machine Learning one strong predictor is sought.

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1 book-chapters:59126b5d0cae96d3a3cefdcfeda9feed sg:abstract Abstract It has been felt for some time that, despite employing different formalisms, being served by their own dedicated research communities and addressing distinct issues of practical interest, problems in Data Mining and Machine Learning connect through deep relationships. The paper [5] has taken a first step towards linking Data Mining and Machine Learning via Combinatorics by showing a correspondence between the problem of finding maximally specific sentences that are interesting in a database, the model of exact learning of monotone boolean functions in computational learning theory and the hyper-graph transversal problem in the combinatorics of finite sets. [5] summarises and concludes a series of valuable Data Mining research on fast discovery of association rules by the levelwise algorithm, series that includes [1, 4, 11]. Intuitively, a Data Mining task may consist of finding many weak predictors in a hypothesis space whereas in Machine Learning one strong predictor is sought.
2 sg:abstractRights OpenAccess
3 sg:bibliographyRights Restricted
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6 sg:copyrightHolder Springer-Verlag Berlin Heidelberg
7 sg:copyrightYear 2004
8 sg:ddsId Chap33
9 sg:doi 10.1007/978-3-540-45240-9_33
10 sg:esmRights OpenAccess
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15 sg:language En
16 sg:license http://scigraph.springernature.com/explorer/license/
17 sg:metadataRights OpenAccess
18 sg:pageFirst 241
19 sg:pageLast 248
20 sg:scigraphId 59126b5d0cae96d3a3cefdcfeda9feed
21 sg:title Soft Learning: A Conceptual Bridge between Data Mining and Machine Learning
22 sg:webpage https://link.springer.com/10.1007/978-3-540-45240-9_33
23 rdf:type sg:BookChapter
24 rdfs:label BookChapter: Soft Learning: A Conceptual Bridge between Data Mining and Machine Learning
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