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» The Tradeoffs of Large Scale Learning
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MIR
2005
ACM
133views Multimedia» more  MIR 2005»
14 years 1 months ago
Probabilistic web image gathering
We propose a new method for automated large scale gathering of Web images relevant to specified concepts. Our main goal is to build a knowledge base associated with as many conce...
Keiji Yanai, Kobus Barnard
ICPR
2008
IEEE
14 years 9 months ago
EK-SVD: Optimized dictionary design for sparse representations
Sparse representations using overcomplete dictionaries are used in a variety of field such as pattern recognition and compression. However, the size of dictionary is usually a tra...
Raazia Mazhar, Paul D. Gader
CVPR
2009
IEEE
15 years 3 months ago
Constrained Marginal Space Learning for Efficient 3D Anatomical Structure Detection in Medical Images
Recently, we proposed marginal space learning (MSL) as a generic approach for automatic detection of 3D anatom- ical structures in many medical imaging modalities. To accurately...
Yefeng Zheng, Bogdan Georgescu, Haibin Ling, Shaoh...
ICDM
2009
IEEE
151views Data Mining» more  ICDM 2009»
13 years 5 months ago
TagLearner: A P2P Classifier Learning System from Collaboratively Tagged Text Documents
The amount of text data on the Internet is growing at a very fast rate. Online text repositories for news agencies, digital libraries and other organizations currently store gigaan...
Haimonti Dutta, Xianshu Zhu, Tushar Mahule, Hillol...
INFFUS
2008
97views more  INFFUS 2008»
13 years 8 months ago
Using classifier ensembles to label spatially disjoint data
act 11 We describe an ensemble approach to learning from arbitrarily partitioned data. The partitioning comes from the distributed process12 ing requirements of a large scale simul...
Larry Shoemaker, Robert E. Banfield, Lawrence O. H...