PUBLICATION DATE

2013-02-17

AUTHORS

Shiliang Sun

TITLE

A survey of multi-view machine learning

ISSUE

7

VOLUME

23

ISSN (print)

0941-0643

ISSN (electronic)

1433-3058

ABSTRACT

Multi-view learning or learning with multiple distinct feature sets is a rapidly growing direction in machine learning with well theoretical underpinnings and great practical success. This paper reviews theories developed to understand the properties and behaviors of multi-view learning and gives a taxonomy of approaches according to the machine learning mechanisms involved and the fashions in which multiple views are exploited. This survey aims to provide an insightful organization of current developments in the field of multi-view learning, identify their limitations, and give suggestions for further research. One feature of this survey is that we attempt to point out specific open problems which can hopefully be useful to promote the research of multi-view machine learning.

How to use: Click on a object to move its position. Double click to open its homepage. Right click to preview its contents.

Download the RDF metadata as:   json-ld nt turtle xml License info


33 TRIPLES      30 PREDICATES      33 URIs      21 LITERALS

Subject Predicate Object
1 articles:f066544fcf1d56885ab0b2e4dbf9585a sg:abstract Abstract Multi-view learning or learning with multiple distinct feature sets is a rapidly growing direction in machine learning with well theoretical underpinnings and great practical success. This paper reviews theories developed to understand the properties and behaviors of multi-view learning and gives a taxonomy of approaches according to the machine learning mechanisms involved and the fashions in which multiple views are exploited. This survey aims to provide an insightful organization of current developments in the field of multi-view learning, identify their limitations, and give suggestions for further research. One feature of this survey is that we attempt to point out specific open problems which can hopefully be useful to promote the research of multi-view machine learning.
2 sg:articleType ReviewPaper
3 sg:coverYear 2013
4 sg:coverYearMonth 2013-12
5 sg:ddsId s00521-013-1362-6
6 sg:ddsIdJournalBrand 521
7 sg:doi 10.1007/s00521-013-1362-6
8 sg:doiLink http://dx.doi.org/10.1007/s00521-013-1362-6
9 sg:hasContributingOrganization grid-institutes:grid.22069.3f
10 sg:hasContribution contributions:2b63600a066643799a7deccd9aacffad
11 sg:hasFieldOfResearchCode anzsrc-for:08
12 anzsrc-for:0801
13 sg:hasJournal journals:3a21858bf4a51235186f9d33eccbe802
14 journals:db33d4382a496aa4231fc989e4b287bd
15 sg:hasJournalBrand journal-brands:238bb913fe5c5128c3967c4c4825ce63
16 sg:indexingDatabase Scopus
17 Web of Science
18 sg:issnElectronic 1433-3058
19 sg:issnPrint 0941-0643
20 sg:issue 7
21 sg:language English
22 sg:license http://scigraph.springernature.com/explorer/license/
23 sg:pageEnd 2038
24 sg:pageStart 2031
25 sg:publicationDate 2013-02-17
26 sg:publicationYear 2013
27 sg:publicationYearMonth 2013-02
28 sg:scigraphId f066544fcf1d56885ab0b2e4dbf9585a
29 sg:title A survey of multi-view machine learning
30 sg:volume 23
31 sg:webpage https://link.springer.com/10.1007/s00521-013-1362-6
32 rdf:type sg:Article
33 rdfs:label Article: A survey of multi-view machine learning
HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular JSON format for linked data.

curl -H 'Accept: application/ld+json' 'http://scigraph.springernature.com/things/articles/f066544fcf1d56885ab0b2e4dbf9585a'

N-Triples is a line-based linked data format ideal for batch operations .

curl -H 'Accept: application/n-triples' 'http://scigraph.springernature.com/things/articles/f066544fcf1d56885ab0b2e4dbf9585a'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'http://scigraph.springernature.com/things/articles/f066544fcf1d56885ab0b2e4dbf9585a'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'http://scigraph.springernature.com/things/articles/f066544fcf1d56885ab0b2e4dbf9585a'






Preview window. Press ESC to close (or click here)


...