Gaussian Logic for Predictive Classification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F11%3A00183396" target="_blank" >RIV/68407700:21230/11:00183396 - isvavai.cz</a>
Result on the web
<a href="http://www.springerlink.com/content/527048g350795uh0" target="_blank" >http://www.springerlink.com/content/527048g350795uh0</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-23783-6_18" target="_blank" >10.1007/978-3-642-23783-6_18</a>
Alternative languages
Result language
angličtina
Original language name
Gaussian Logic for Predictive Classification
Original language description
We describe a statistical relational learning framework called Gaussian Logic capable to work efficiently with combinations of relational and numerical data. The framework assumes that, for a fixed relational structure, the numerical data can be modelledby a multivariate normal distribution. We demonstrate how the Gaussian Logic framework can be applied to predictive classification problems. In experiments, we first show an application of the framework for the prediction of DNA-binding propensity of proteins. Next, we show how the Gaussian Logic framework can be used to find motifs describing highly correlated gene groups in gene-expression data which are then used in a set-level-based classification method.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Machine Learning and Knowledge Discovery in Databases
ISBN
978-3-642-23782-9
ISSN
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e-ISSN
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Number of pages
16
Pages from-to
277-292
Publisher name
Springer
Place of publication
Berlin
Event location
Athens
Event date
Sep 5, 2011
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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