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» Max-Margin Additive Classifiers for Detection
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ECCV
2010
Springer
13 years 7 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof
BMCBI
2008
114views more  BMCBI 2008»
13 years 7 months ago
Identifying a few foot-and-mouth disease virus signature nucleotide strings for computational genotyping
Background: Serotypes of the Foot-and-Mouth disease viruses (FMDVs) were generally determined by biological experiments. The computational genotyping is not well studied even with...
Guohui Lin, Zhipeng Cai, Junfeng Wu, Xiu-Feng Wan,...
BMCBI
2005
87views more  BMCBI 2005»
13 years 7 months ago
ProfNet, a method to derive profile-profile alignment scoring functions that improves the alignments of distantly related protei
Background: Profile-profile methods have been used for some years now to detect and align homologous proteins. The best such methods use information from the background distributi...
Tomas Ohlson, Arne Elofsson
ICCV
2007
IEEE
14 years 9 months ago
Gradient Feature Selection for Online Boosting
Boosting has been widely applied in computer vision, especially after Viola and Jones's seminal work [23]. The marriage of rectangular features and integral-imageenabled fast...
Ting Yu, Xiaoming Liu 0002
MOBICOM
2010
ACM
13 years 7 months ago
Physical-layer identification of UHF RFID tags
In this work, we study physical-layer identification of passive UHF RFID tags. We collect signals from a population of 70 tags using a purpose-built reader and we analyze time dom...
Davide Zanetti, Boris Danev, Srdjan Capkun