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» Learning from Multiple Sources of Inaccurate Data
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CVPR
2011
IEEE
13 years 2 months ago
Learning Effective Human Pose Estimation from Inaccurate Annotation
The task of 2-D articulated human pose estimation in natural images is extremely challenging due to the high level of variation in human appearance. These variations arise from di...
Sam Johnson, Mark Everingham
DATAMINE
2006
139views more  DATAMINE 2006»
13 years 5 months ago
Discovering Classification from Data of Multiple Sources
In many large e-commerce organizations, multiple data sources are often used to describe the same customers, thus it is important to consolidate data of multiple sources for intell...
Charles X. Ling, Qiang Yang
SDM
2012
SIAM
252views Data Mining» more  SDM 2012»
11 years 8 months ago
Learning from Heterogeneous Sources via Gradient Boosting Consensus
Multiple data sources containing different types of features may be available for a given task. For instance, users’ profiles can be used to build recommendation systems. In a...
Xiaoxiao Shi, Jean-François Paiement, David...
PRIB
2009
Springer
209views Bioinformatics» more  PRIB 2009»
14 years 3 days ago
Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm
For many biomedical modelling tasks a number of different types of data may influence predictions made by the model. An established approach to pursuing supervised learning with ...
Yiming Ying, Colin Campbell, Theodoros Damoulas, M...
KDD
2004
ACM
164views Data Mining» more  KDD 2004»
14 years 6 months ago
Ordering patterns by combining opinions from multiple sources
Pattern ordering is an important task in data mining because the number of patterns extracted by standard data mining algorithms often exceeds our capacity to manually analyze the...
Pang-Ning Tan, Rong Jin