dc.contributor.author |
Barnard, E
|
|
dc.contributor.author |
Van der Walt, Christiaan M
|
|
dc.contributor.author |
Davel, M
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|
dc.contributor.author |
Van Heerden, C
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|
dc.contributor.author |
Senekal, FP
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|
dc.contributor.author |
Naidoo, T
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|
dc.date.accessioned |
2012-02-15T08:41:05Z |
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dc.date.available |
2012-02-15T08:41:05Z |
|
dc.date.issued |
2009-11 |
|
dc.identifier.citation |
Barnard, E, Van der Walt, C, Davel, M et al. Learning structured representations of data. 20th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Stellenbosch, South Africa, 30 November-01 December 2009, pp 1-6 |
en_US |
dc.identifier.isbn |
978-0-7992-2356-9 |
|
dc.identifier.uri |
http://www.prasa.org/proceedings/2009/prasa09-01.pdf
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/5570
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|
dc.description |
20th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Stellenbosch, South Africa, 30 November-01 December 2009 |
en_US |
dc.description.abstract |
Bayesian networks have shown themselves to be useful tools for the analysis and modelling of large data sets. However, their complete generality leads to computational and modelling complexities that have limited their applicability. We propose an approach to simplify and constrain Bayesian networks that strikes a more useful compromise between generality and tractability. These constrained graphical will allow us to build computationally tractable models for large high-dimensional data sets. We also describe examples of data sets drawn from image and speech processing on which can (1) further explore this constrained set of graphical models, and (2) analyse their performance as a general-purpose statistical data analysis tool. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
PRASA |
en_US |
dc.subject |
Data sets |
en_US |
dc.subject |
Data analysis |
en_US |
dc.subject |
Generality |
en_US |
dc.subject |
Tractability |
en_US |
dc.subject |
Bayesian networks |
en_US |
dc.title |
Learning structured representations of data |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Barnard, E., Van der Walt, C. M., Davel, M., Van Heerden, C., Senekal, F., & Naidoo, T. (2009). Learning structured representations of data. PRASA. http://hdl.handle.net/10204/5570 |
en_ZA |
dc.identifier.chicagocitation |
Barnard, E, Christiaan M Van der Walt, M Davel, C Van Heerden, FP Senekal, and T Naidoo. "Learning structured representations of data." (2009): http://hdl.handle.net/10204/5570 |
en_ZA |
dc.identifier.vancouvercitation |
Barnard E, Van der Walt CM, Davel M, Van Heerden C, Senekal F, Naidoo T, Learning structured representations of data; PRASA; 2009. http://hdl.handle.net/10204/5570 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Barnard, E
AU - Van der Walt, Christiaan M
AU - Davel, M
AU - Van Heerden, C
AU - Senekal, FP
AU - Naidoo, T
AB - Bayesian networks have shown themselves to be useful tools for the analysis and modelling of large data sets. However, their complete generality leads to computational and modelling complexities that have limited their applicability. We propose an approach to simplify and constrain Bayesian networks that strikes a more useful compromise between generality and tractability. These constrained graphical will allow us to build computationally tractable models for large high-dimensional data sets. We also describe examples of data sets drawn from image and speech processing on which can (1) further explore this constrained set of graphical models, and (2) analyse their performance as a general-purpose statistical data analysis tool.
DA - 2009-11
DB - ResearchSpace
DP - CSIR
KW - Data sets
KW - Data analysis
KW - Generality
KW - Tractability
KW - Bayesian networks
LK - https://researchspace.csir.co.za
PY - 2009
SM - 978-0-7992-2356-9
T1 - Learning structured representations of data
TI - Learning structured representations of data
UR - http://hdl.handle.net/10204/5570
ER -
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en_ZA |