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ICIP
2009
IEEE
13 years 6 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
ICCV
2011
IEEE
12 years 9 months ago
Incremental On-line Semi-supervised Learning for Segmenting the Left Ventricle of the Heart from Ultrasound Data
Recently, there has been an increasing interest in the investigation of statistical pattern recognition models for the fully automatic segmentation of the left ventricle (LV) of t...
Gustavo Carneiro, Jacinto C. Nascimento
KDD
1999
ACM
199views Data Mining» more  KDD 1999»
14 years 1 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
ICASSP
2011
IEEE
13 years 19 days ago
Denoising sparse noise via online dictionary learning
The idea of learning overcomplete dictionaries based on the paradigm of compressive sensing has found numerous applications, among which image denoising is considered one of the m...
Anoop Cherian, Suvrit Sra, Nikolaos Papanikolopoul...
HICSS
2003
IEEE
124views Biometrics» more  HICSS 2003»
14 years 2 months ago
Community Building and Virtual Teamwork in an Online Learning Environment
In the world of OTIS, an online Internet School for occupational therapists, students from four European countries were encouraged to work collaboratively through problem based le...
Martin D. Beer, Frances Slack, Gillian Armitt