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CVPR
2007
IEEE
13 years 12 months ago
Matrix-Structural Learning (MSL) of Cascaded Classifier from Enormous Training Set
Aiming at the problem when both positive and negative training set are enormous, this paper proposes a novel Matrix-Structural Learning (MSL) method, as an extension to Viola and ...
Shengye Yan, Shiguang Shan, Xilin Chen, Wen Gao, J...
ICTAI
2006
IEEE
13 years 11 months ago
Learning to Predict Salient Regions from Disjoint and Skewed Training Sets
We present an ensemble learning approach that achieves accurate predictions from arbitrarily partitioned data. The partitions come from the distributed processing requirements of ...
Larry Shoemaker, Robert E. Banfield, Lawrence O. H...
FUIN
2002
123views more  FUIN 2002»
13 years 5 months ago
Learning Rough Set Classifiers from Gene Expressions and Clinical Data
Biological research is currently undergoing a revolution. With the advent of microarray technology the behavior of thousands of genes can be measured simultaneously. This capabilit...
Herman Midelfart, Henryk Jan Komorowski, Kristin N...
HPDC
2010
IEEE
13 years 6 months ago
Lessons learned from moving earth system grid data sets over a 20 Gbps wide-area network
In preparation for the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report, the climate community will run the Coupled Model Intercomparison Project phase 5 (...
Rajkumar Kettimuthu, Alex Sim, Dan Gunter, Bill Al...
JCST
2006
128views more  JCST 2006»
13 years 5 months ago
Multi-Instance Learning from Supervised View
Abstract In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. This pa...
Zhi-Hua Zhou