PUBLICATION DATE

2007-05-03

AUTHORS

Ying-Hong Li, Dong-Fang Zhang, Xun-Kai Wei, Yu-Fei Li

TITLE

Enclosing machine learning: concepts and algorithms

ISSUE

3

VOLUME

17

ISSN (print)

0941-0643

ISSN (electronic)

1433-3058

ABSTRACT

A novel machine learning paradigm, i.e., enclosing machine learning based on regular geometric shapes was proposed. First, it adopted regular minimum volume enclosing and bounding geometric shapes (sphere, ellipsoid, box) or their unions and so on to obtain one class description model. Second, Data description, two class classification, learning algorithms based on the one class description model were presented. The most obvious feature was that enclosing machine learning emphasized one class description and learning. To illustrate the concepts and algorithms, a minimum volume enclosing ellipsoid (MVEE) case for enclosing machine learning was then investigated in detail. Implementation algorithms for enclosing machine learning based on MVEE were presented. Subsequently, we validate the performances of MVEE learners using real world datasets. For novelty detection, a benchmark ball bearing dataset is adopted. For pattern classification, a benchmark iris dataset is investigated. The performance results show that our proposed method is comparable even better than Support Vector Machines (SVMs) in the datasets studied.

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36 TRIPLES      30 PREDICATES      37 URIs      22 LITERALS

Subject Predicate Object
1 articles:9a3c080e0372720c85e5ded1624ff603 sg:abstract Abstract A novel machine learning paradigm, i.e., enclosing machine learning based on regular geometric shapes was proposed. First, it adopted regular minimum volume enclosing and bounding geometric shapes (sphere, ellipsoid, box) or their unions and so on to obtain one class description model. Second, Data description, two class classification, learning algorithms based on the one class description model were presented. The most obvious feature was that enclosing machine learning emphasized one class description and learning. To illustrate the concepts and algorithms, a minimum volume enclosing ellipsoid (MVEE) case for enclosing machine learning was then investigated in detail. Implementation algorithms for enclosing machine learning based on MVEE were presented. Subsequently, we validate the performances of MVEE learners using real world datasets. For novelty detection, a benchmark ball bearing dataset is adopted. For pattern classification, a benchmark iris dataset is investigated. The performance results show that our proposed method is comparable even better than Support Vector Machines (SVMs) in the datasets studied.
2 sg:articleType OriginalPaper
3 sg:coverYear 2008
4 sg:coverYearMonth 2008-06
5 sg:ddsId s00521-007-0113-y
6 sg:ddsIdJournalBrand 521
7 sg:doi 10.1007/s00521-007-0113-y
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21 sg:issnElectronic 1433-3058
22 sg:issnPrint 0941-0643
23 sg:issue 3
24 sg:language English
25 sg:license http://scigraph.springernature.com/explorer/license/
26 sg:pageEnd 243
27 sg:pageStart 237
28 sg:publicationDate 2007-05-03
29 sg:publicationYear 2007
30 sg:publicationYearMonth 2007-05
31 sg:scigraphId 9a3c080e0372720c85e5ded1624ff603
32 sg:title Enclosing machine learning: concepts and algorithms
33 sg:volume 17
34 sg:webpage https://link.springer.com/10.1007/s00521-007-0113-y
35 rdf:type sg:Article
36 rdfs:label Article: Enclosing machine learning: concepts and algorithms
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