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ICIP
2009
IEEE
13 years 5 months ago
An incremental extremely random forest classifier for online learning and tracking
Decision trees have been widely used for online learning classification. Many approaches usually need large data stream to finish decision trees induction, as show notable limitat...
Aiping Wang, Guowei Wan, Zhiquan Cheng, Sikun Li
RECOMB
2001
Springer
14 years 8 months ago
Finding motifs using random projections
motif discovery problem abstracts the task of discovering short, conserved sites in genomic DNA. Pevzner and Sze recently described a precise combinatorial formulation of motif di...
Jeremy Buhler, Martin Tompa
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
CVPR
2010
IEEE
14 years 1 months ago
Semantic Context Modeling with Maximal Margin Conditional Random Fields for Automatic Image Annotation
Context modeling for Vision Recognition and Automatic Image Annotation (AIA) has attracted increasing attentions in recent years. For various contextual information and resources,...
Yu Xiang, Xiangdong Zhou, Zuotao Liu, Tat-seng chu...
KES
2008
Springer
13 years 7 months ago
Incremental evolution of a signal classification hardware architecture for prosthetic hand control
Evolvable Hardware (EHW) is a new method for designing electronic circuits. However, there are several problems to solve for making high performance systems. One is the limited sca...
Jim Torresen