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» Efficient Discovery of Confounders in Large Data Sets
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KDD
2008
ACM
167views Data Mining» more  KDD 2008»
16 years 6 months ago
A sequential dual method for large scale multi-class linear svms
Efficient training of direct multi-class formulations of linear Support Vector Machines is very useful in applications such as text classification with a huge number examples as w...
S. Sathiya Keerthi, S. Sundararajan, Kai-Wei Chang...
AUSAI
2003
Springer
15 years 11 months ago
Efficiently Mining Frequent Patterns from Dense Datasets Using a Cluster of Computers
Efficient mining of frequent patterns from large databases has been an active area of research since it is the most expensive step in association rules mining. In this paper, we pr...
Yudho Giri Sucahyo, Raj P. Gopalan, Amit Rudra
BMCBI
2006
144views more  BMCBI 2006»
15 years 6 months ago
Development and implementation of an algorithm for detection of protein complexes in large interaction networks
Background: After complete sequencing of a number of genomes the focus has now turned to proteomics. Advanced proteomics technologies such as two-hybrid assay, mass spectrometry e...
Md. Altaf-Ul-Amin, Yoko Shinbo, Kenji Mihara, Ken ...
ICDM
2009
IEEE
197views Data Mining» more  ICDM 2009»
15 years 4 months ago
A Linear-Time Graph Kernel
The design of a good kernel is fundamental for knowledge discovery from graph-structured data. Existing graph kernels exploit only limited information about the graph structures bu...
Shohei Hido, Hisashi Kashima
BMCBI
2008
214views more  BMCBI 2008»
15 years 6 months ago
Accelerating String Set Matching in FPGA Hardware for Bioinformatics Research
Background: This paper describes techniques for accelerating the performance of the string set matching problem with particular emphasis on applications in computational proteomic...
Yoginder S. Dandass, Shane C. Burgess, Mark Lawren...