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» Learning from Highly Structured Data by Decomposition
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IEEEICCI
2009
IEEE
14 years 7 months ago
Learning from an ensemble of Receptive Fields
Abstract-In this paper, we construct a neural-inspired computational model based on the representational capabilities of receptive fields. The proposed model, known as Shape Encodi...
Hanlin Goh, Joo Hwe Lim, Chai Quek
ICML
2003
IEEE
15 years 10 months ago
On Kernel Methods for Relational Learning
Kernel methods have gained a great deal of popularity in the machine learning community as a method to learn indirectly in highdimensional feature spaces. Those interested in rela...
Chad M. Cumby, Dan Roth
BMCBI
2006
101views more  BMCBI 2006»
14 years 9 months ago
SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms
Background: The development of algorithms to infer the structure of gene regulatory networks based on expression data is an important subject in bioinformatics research. Validatio...
Tim Van den Bulcke, Koen Van Leemput, Bart Naudts,...
LREC
2010
145views Education» more  LREC 2010»
14 years 11 months ago
Generic Ontology Learners on Application Domains
In ontology learning from texts, we have ontology-rich domains where we have large structured domain knowledge repositories or we have large general corpora with large general str...
Francesca Fallucchi, Maria Teresa Pazienza, Fabio ...
IV
2010
IEEE
140views Visualization» more  IV 2010»
14 years 8 months ago
GVIS: An Integrating Infrastructure for Adaptively Mashing up User Data from Different Sources
In this article we present an infrastructure for creating mash up visual representations of the user profile that combines data from different sources. We explored this approach ...
Luca Mazzola, Riccardo Mazza