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ICIP
2009
IEEE
14 years 11 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
KDD
1999
ACM
199views Data Mining» more  KDD 1999»
15 years 6 months ago
The Application of AdaBoost for Distributed, Scalable and On-Line Learning
We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
Wei Fan, Salvatore J. Stolfo, Junxin Zhang
AGILEDC
2003
IEEE
15 years 7 months ago
Evolving Agile in the Enterprise: Implementing XP on a Grand Scale
How can XP or other agile methods be used in large corporate IT shops? One large company found out, making XP the official corporate software development methodology for all proje...
Michael K. Spayd
ICPR
2008
IEEE
16 years 3 months ago
GPU-boosted online image matching
Matching feature points between images is a key point in many Computer Vision tasks. As the number of images increases, this rapidly becomes a bottleneck. We here present how to u...
Alexandre Chariot, Renaud Keriven
TNN
2008
105views more  TNN 2008»
15 years 1 months ago
Incremental Learning of Chunk Data for Online Pattern Classification Systems
This paper presents a pattern classification system in which feature extraction and classifier learning are simultaneously carried out not only online but also in one pass where tr...
Seiichi Ozawa, Shaoning Pang, Nikola K. Kasabov