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BMCBI
2006
119views more  BMCBI 2006»
15 years 6 months ago
LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates
Background: Non-negative matrix factorisation (NMF), a machine learning algorithm, has been applied to the analysis of microarray data. A key feature of NMF is the ability to iden...
Guoli Wang, Andrew V. Kossenkov, Michael F. Ochs
CVPR
2007
IEEE
16 years 8 months ago
Recognizing Groceries in situ Using in vitro Training Data
The problem of using pictures of objects captured under ideal imaging conditions (here referred to as in vitro) to recognize objects in natural environments (in situ) is an emergi...
Michele Merler, Carolina Galleguillos, Serge Belon...
SIGMOD
2009
ACM
269views Database» more  SIGMOD 2009»
16 years 6 months ago
Efficient approximate entity extraction with edit distance constraints
Named entity recognition aims at extracting named entities from unstructured text. A recent trend of named entity recognition is finding approximate matches in the text with respe...
Wei Wang 0011, Chuan Xiao, Xuemin Lin, Chengqi Zha...

Book
2911views
17 years 5 months ago
Shape Analysis and Classification: Theory and Practice
"Advances in shape analysis impact a wide range of disciplines, from mathematics and engineering to medicine, archeology, and art. Anyone just entering the field, however, may...
Roberto M. Cesar Jr

Book
5396views
17 years 5 months ago
Markov Random Field Modeling in Computer Vision
Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms sy...
Stan Z. Li