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SIGIR
2006
ACM
14 years 1 months ago
Large scale semi-supervised linear SVMs
Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In...
Vikas Sindhwani, S. Sathiya Keerthi
MMDB
2004
ACM
148views Multimedia» more  MMDB 2004»
14 years 24 days ago
A unified framework for image database clustering and content-based retrieval
With the proliferation of image data, the need to search and retrieve images efficiently and accurately from a large image database or a collection of image databases has drastica...
Mei-Ling Shyu, Shu-Ching Chen, Min Chen, Chengcui ...
HPDC
2010
IEEE
13 years 8 months ago
Modeling sequence and function similarity between proteins for protein functional annotation
A common task in biological research is to predict function for proteins by comparing sequences between proteins of known and unknown function. This is often done using pair-wise ...
Roger Higdon, Brenton Louie, Eugene Kolker
ALMOB
2007
74views more  ALMOB 2007»
13 years 7 months ago
Evaluating deterministic motif significance measures in protein databases
Background: Assessing the outcome of motif mining algorithms is an essential task, as the number of reported motifs can be very large. Significance measures play a central role in...
Pedro Gabriel Ferreira, Paulo J. Azevedo
BMCBI
2006
127views more  BMCBI 2006»
13 years 7 months ago
Automatic discovery of cross-family sequence features associated with protein function
Background: Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterised protein families and in comparative genomic...
Markus Brameier, Josien Haan, Andrea Krings, Rober...