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» Learning from Highly Structured Data by Decomposition
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ICPR
2008
IEEE
15 years 11 months ago
Supervised learning of a generative model for edge-weighted graphs
This paper addresses the problem of learning archetypal structural models from examples. To this end we define a generative model for graphs where the distribution of observed nod...
Andrea Torsello, David L. Dowe
BMCBI
2008
134views more  BMCBI 2008»
14 years 10 months ago
Identification of transcription factor contexts in literature using machine learning approaches
Background: Availability of information about transcription factors (TFs) is crucial for genome biology, as TFs play a central role in the regulation of gene expression. While man...
Hui Yang, Goran Nenadic, John A. Keane
PAKDD
2004
ACM
143views Data Mining» more  PAKDD 2004»
15 years 3 months ago
Compact Dual Ensembles for Active Learning
Generic ensemble methods can achieve excellent learning performance, but are not good candidates for active learning because of their different design purposes. We investigate how...
Amit Mandvikar, Huan Liu, Hiroshi Motoda
BMCBI
2006
100views more  BMCBI 2006»
14 years 10 months ago
STAR: predicting recombination sites from amino acid sequence
Background: Designing novel proteins with site-directed recombination has enormous prospects. By locating effective recombination sites for swapping sequence parts, the probabilit...
Denis C. Bauer, Mikael Bodén, Ricarda Thier...
KDD
2005
ACM
178views Data Mining» more  KDD 2005»
15 years 3 months ago
Failure detection and localization in component based systems by online tracking
The increasing complexity of today’s systems makes fast and accurate failure detection essential for their use in mission-critical applications. Various monitoring methods provi...
Haifeng Chen, Guofei Jiang, Cristian Ungureanu, Ke...